Datasets:

Merserk
/

Krea-2-Turbo-Checkpoint-Format-Benchmark

@@ -2,7 +2,7 @@ cff-version: 1.2.0
 message: If you use this benchmark, please cite it.
 title: Krea 2 Turbo ComfyUI Format Fidelity Benchmark
 type: dataset
-version: 1.0.0
 date-released: 2026-07-13
 authors:
   - name: Krea 2 Turbo Formats Benchmark Contributors

 message: If you use this benchmark, please cite it.
 title: Krea 2 Turbo ComfyUI Format Fidelity Benchmark
 type: dataset
+version: 2.0.0
 date-released: 2026-07-13
 authors:
   - name: Krea 2 Turbo Formats Benchmark Contributors

README.md CHANGED Viewed

@@ -2,16 +2,16 @@
 language:
 - en
 license: cc-by-4.0
-pretty_name: Krea 2 Turbo ComfyUI Format Fidelity Benchmark
-size_categories:
-- n<1K
-configs:
-- config_name: default
-  default: true
-  data_files:
-  - split: train
-    path: data/train/**
-task_categories:
 - text-to-image
 tags:
 - image
@@ -25,35 +25,41 @@ tags:
 - int8
 - mxfp8
 - nvfp4
 ---
 # Krea 2 Turbo ComfyUI Format Fidelity Benchmark
-This release is a paired, deterministic comparison of the BF16, FP8 Scaled, INT8 ConvRot, MXFP8, and NVFP4 Krea 2 Turbo checkpoints through native ComfyUI execution. It contains 150 scored 1024×1024 images, all saved float32 decoded tensors and final latents, every denoising trajectory, raw metric tables, scored telemetry, statistical comparisons, and reproducibility code.
-![Five-format contact sheet](comparison_sheets/contact_sheet_replicate0.png)
 ## Main result
 1. **BF16** is the highest-fidelity reference because it is the unquantized published checkpoint.
-2. **INT8 ConvRot** is the best quantized checkpoint by the preregistered LPIPS-Alex endpoint and also leads the other quantized formats on DISTS, DINO similarity, final-latent relative L2, and reconstructed-weight SNR.
-3. **MXFP8** ranks second among quantized formats for fidelity on this campaign.
-4. **FP8 Scaled** ranks third among quantized formats.
-5. **NVFP4** is the smallest checkpoint, but it has the largest measured fidelity loss.
 No weighted composite score is used. See [the full technical report](TECHNICAL_REPORT.md) and [`tables/decision_table.csv`](tables/decision_table.csv).
 ## Important performance limitation
-The test GPU was an NVIDIA GeForce RTX 4060 Ti 16 GB (SM 8.9). In the tested ComfyUI/comfy-kitchen runtime, MXFP8 and NVFP4 native fast matrix multiplication requires SM 10.0, so their measured Ada timings used fallback/dequantized execution and must not be projected to Blackwell hardware.
 ## Dataset organization
 - `data/train/metadata.jsonl` is the Hugging Face ImageFolder index. Each row links an image to its prompt, seed, format, checkpoint provenance, raw scientific artifacts, and flattened metric values.
 - `raw/` contains `decoded_float32.npy`, `final_latent_float32.npy`, `trajectory.npz`, and capture `metadata.json` for every scored run.
 - `metrics/` contains raw per-image, per-parameter, paired-statistics, summary, trajectory, latency, and performance tables.
-- `comparison_sheets/` contains five-format sheets, BF16-relative difference maps, and automatically selected detail crops.
-- `telemetry/` contains only scored GPU and system telemetry.
 - `provenance/` contains sanitized environment, model, run, and release manifests.
 - `reproduction/` contains the ComfyUI workflows, custom capture node, benchmark driver, analyzers, model downloader, and exact instructions.
@@ -62,23 +68,23 @@ Load the image table locally:
 ```python
 from datasets import load_dataset
-dataset = load_dataset("imagefolder", data_dir="data", split="train")
-print(dataset.num_rows)  # 150
 ```
 ## Fixed inference controls
-- 15 prompts × 2 deterministic seeds × 5 checkpoint formats = 150 scored images
 - 1024×1024, batch size 1
 - 8 steps, CFG 1.0, Euler sampler, simple scheduler, denoise 1.0
 - Shared `qwen3vl_4b_bf16.safetensors` text encoder and `qwen_image_vae.safetensors` VAE
 - Zeroed positive conditioning used as negative conditioning
 - No prompt rewriting, LoRAs, previews, upscaling, or post-processing
-- Identical initial-noise SHA-256 for every five-format prompt/seed group
 ## Reproduce or verify
-Follow [`reproduction/README.md`](reproduction/README.md). A faithful full rerun downloads 77.536 GiB of upstream model files and should have at least 110 GiB of free disk space. The published benchmark was tested on Windows 11 with a 16 GB NVIDIA GPU; lower-memory configurations are not certified.
 Validate this downloaded release:

 language:
 - en
 license: cc-by-4.0
+pretty_name: Krea 2 Turbo ComfyUI Format Fidelity Benchmark
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: train
+    path: data/train/**
+task_categories:
 - text-to-image
 tags:
 - image
 - int8
 - mxfp8
 - nvfp4
+- int4
+- gguf
+- q8-0
+- q4-k-m
 ---
 # Krea 2 Turbo ComfyUI Format Fidelity Benchmark
+This release is a paired, deterministic comparison of eight Krea 2 Turbo checkpoint formats in ComfyUI: BF16, FP8 Scaled, INT8 ConvRot, MXFP8, NVFP4, INT4 ConvRot W4A4, GGUF Q8_0, and GGUF Q4_K_M. It contains 240 scored 1024×1024 images, saved float32 decoded tensors and final latents, every denoising trajectory, raw metric tables, telemetry, statistical comparisons, and reproduction code.
+![Eight-format contact sheet](comparison_sheets/contact_sheet_replicate0.png)
 ## Main result
 1. **BF16** is the highest-fidelity reference because it is the unquantized published checkpoint.
+2. **INT8 ConvRot** narrowly leads the preregistered LPIPS-Alex endpoint. **GGUF Q8_0** is statistically tied with it on that endpoint (Holm-adjusted permutation p=0.9067) and leads INT8 on DISTS, DINO similarity, and reconstructed-weight SNR.
+3. **GGUF Q8_0** is the highest-fidelity of the three added formats.
+4. **GGUF Q4_K_M** is a storage-quality compromise: 6.972 GiB and much closer to BF16 than INT4 ConvRot, but slow on this Ada GPU under ComfyUI-GGUF dequantized execution.
+5. **INT4 ConvRot W4A4** is the smallest checkpoint and fastest of the three additions on this RTX 4060 Ti, but has the largest measured fidelity loss.
 No weighted composite score is used. See [the full technical report](TECHNICAL_REPORT.md) and [`tables/decision_table.csv`](tables/decision_table.csv).
 ## Important performance limitation
+The test GPU was an NVIDIA GeForce RTX 4060 Ti 16 GB (SM 8.9). MXFP8 and NVFP4 native fast matrix multiplication requires SM 10.0 in the tested runtime. GGUF Q8_0 and Q4_K_M use the pinned ComfyUI-GGUF on-demand dequantized matrix-multiplication path. These Ada timings must not be projected to native Blackwell execution.
+The original five formats and the three additions were measured in separate sessions. Five unscored BF16 and INT8 ConvRot bridge repeats quantify drift. INT8 crossed the preregistered 5% drift threshold, so exact old-versus-new timing comparisons remain caveated; fidelity comparisons remain paired to the same saved BF16 prompt/seed outputs.
 ## Dataset organization
 - `data/train/metadata.jsonl` is the Hugging Face ImageFolder index. Each row links an image to its prompt, seed, format, checkpoint provenance, raw scientific artifacts, and flattened metric values.
 - `raw/` contains `decoded_float32.npy`, `final_latent_float32.npy`, `trajectory.npz`, and capture `metadata.json` for every scored run.
 - `metrics/` contains raw per-image, per-parameter, paired-statistics, summary, trajectory, latency, and performance tables.
+- `comparison_sheets/` contains eight-format sheets, BF16-relative difference maps, and automatically selected detail crops.
+- `telemetry/` contains scored GPU/system telemetry plus the BF16/INT8 bridge telemetry.
 - `provenance/` contains sanitized environment, model, run, and release manifests.
 - `reproduction/` contains the ComfyUI workflows, custom capture node, benchmark driver, analyzers, model downloader, and exact instructions.
 ```python
 from datasets import load_dataset
+dataset = load_dataset("Merserk/Krea-2-Turbo-Checkpoint-Format-Benchmark", split="train")
+print(dataset.num_rows)  # 240
 ```
 ## Fixed inference controls
+- 15 prompts × 2 deterministic seeds × 8 checkpoint formats = 240 scored images
 - 1024×1024, batch size 1
 - 8 steps, CFG 1.0, Euler sampler, simple scheduler, denoise 1.0
 - Shared `qwen3vl_4b_bf16.safetensors` text encoder and `qwen_image_vae.safetensors` VAE
 - Zeroed positive conditioning used as negative conditioning
 - No prompt rewriting, LoRAs, previews, upscaling, or post-processing
+- Identical initial-noise SHA-256 for every eight-format prompt/seed group
 ## Reproduce or verify
+Follow [`reproduction/README.md`](reproduction/README.md). A complete rerun downloads about 104 GiB of upstream model files and should have at least 150 GiB free for models, captures, analysis caches, and reports. The published benchmark was tested on Windows 11 with a 16 GB NVIDIA GPU; lower-memory configurations are not certified.
 Validate this downloaded release:

TECHNICAL_REPORT.md CHANGED Viewed

@@ -6,9 +6,11 @@
 - **Highest model fidelity:** BF16, because it is the unquantized reference checkpoint used for all paired comparisons.
 - **Best quantized BF16 fidelity by the preregistered primary endpoint (LPIPS-Alex):** INT8 ConvRot.
-- **Primary-endpoint separation from the next quantized format:** statistically separated after Holm correction.
-- **Smallest checkpoint:** NVFP4.
 - **Ada performance warning:** MXFP8 and NVFP4 use fallback/dequantized matrix multiplication on this SM 8.9 GPU; their recorded speed must not be projected to Blackwell hardware.
 - Automated results establish original-model fidelity, prompt alignment, and measurable artifact behavior. They do not establish universal human aesthetic preference.
 ### Decision table
@@ -20,9 +22,20 @@
 | INT8 ConvRot | 12.566 | 0.041864 | 0.026796 | 0.983833 | 0.132571 | 41.157030 | 12.417 | 15165.0 | 1944.8 |
 | MXFP8 | 12.603 | 0.071229 | 0.035132 | 0.979443 | 0.201900 | 31.604820 | 31.929 | 14750.0 | 4142.8 |
 | NVFP4 | 7.147 | 0.205124 | 0.084436 | 0.934769 | 0.381361 | 20.600707 | 24.925 | 14459.0 | 3679.3 |
 The decision table is multidimensional by design. No weighted composite score is used.
 ## Test system and immutable controls
 - GPU: `NVIDIA GeForce RTX 4060 Ti; driver 610.74; 16380 MiB; compute capability 8.9`
@@ -33,17 +46,20 @@ The decision table is multidimensional by design. No weighted composite score is
 - Sampler: euler; scheduler: simple; steps: 8; CFG: 1.0; denoise: 1.0
 - Shared text encoder: `qwen3vl_4b_bf16.safetensors`; shared VAE: `qwen_image_vae.safetensors`
 - Negative conditioning is a zeroed copy of positive conditioning. Prompt rewriting, LoRAs, previews, upscaling, and post-processing are disabled.
-- Each prompt/seed pair uses the same initial-noise tensor SHA-256 across all five formats.
 ## Checkpoint structure
-| Format | File | SHA-256 | GiB | Tensors | Stored dtypes | Metadata |
-| --- | --- | --- | --- | --- | --- | --- |
-| BF16 | krea2_turbo_bf16.safetensors | 78bbf8f4165eda19cea3cb06c78089221932a39e2eed8af9da741f942c47ffb3 | 24.478 | 430 | BF16: 256<br>F32: 174 | none |
-| FP8 Scaled | krea2_turbo_fp8_scaled.safetensors | eb4dd8c612cfd10f64f25b057e6e6bbcb5737c94a7372177e456dbf7579502f1 | 12.239 | 686 | BF16: 174<br>F32: 256<br>F8_E4M3: 256 | _quantization_metadata, format |
-| INT8 ConvRot | krea2_turbo_int8_convrot.safetensors | 8e4eeda70dd5037ab1ba2bef6b417f9f901e26093117cf397f741fc1fdaaf3f1 | 12.566 | 878 | BF16: 76<br>F32: 354<br>I8: 224<br>U8: 224 | none |
-| MXFP8 | krea2_turbo_mxfp8.safetensors | 4c09131a442c3a01e95f934b43c76c1e691b9f683694a7b68d6fc707d5ecfce3 | 12.603 | 686 | BF16: 174<br>F8_E4M3: 256<br>U8: 256 | _quantization_metadata, format |
-| NVFP4 | krea2_turbo_nvfp4.safetensors | 61527003b2d537055494d01bc8efe51d6e86e64192ba23e3721a5647231fe394 | 7.147 | 942 | BF16: 174<br>F32: 256<br>F8_E4M3: 256<br>U8: 256 | _quantization_metadata, format |
 ### What the formats actually do
@@ -52,6 +68,9 @@ The decision table is multidimensional by design. No weighted composite score is
 - **INT8 ConvRot:** 224 row-wise INT8 linear weights with group-size-256 online/offline Hadamard rotation. Sensitive input/output, conditioning, and text-fusion components remain higher precision.
 - **MXFP8:** E4M3 values with E8M0 power-of-two block scales over 32-element blocks. It marks 256 linear layers; native fast multiplication requires SM 10.0 in this runtime.
 - **NVFP4:** packed 4-bit floating-point values with per-tensor and 16-element block scales. It marks 256 linear layers; native fast multiplication likewise requires SM 10.0.
 ## Weight reconstruction fidelity
@@ -64,6 +83,9 @@ Every logical parameter was reconstructed with the same native comfy-kitchen lay
 | INT8 ConvRot | 430 | 224 | 0.000277 | 0.000387 | 0.008753 | 0.999961693 | 41.157 | 0.091442 |
 | MXFP8 | 430 | 256 | 0.000721 | 0.001163 | 0.026288 | 0.999654434 | 31.605 | 0.250000 |
 | NVFP4 | 430 | 256 | 0.002923 | 0.004129 | 0.093318 | 0.995646025 | 20.601 | 0.531250 |
 Full per-parameter results, including p50/p95/p99/p99.9 error samples and subsystem grouping, are in [`metrics/weight_parameters_all.csv`](metrics/weight_parameters_all.csv).
@@ -78,8 +100,11 @@ BF16 is a behavioral reference, not a real photograph. Low LPIPS/DISTS and high
 | INT8 ConvRot | 0.041864 | 0.050027 | 0.026796 | 0.956164 | 0.983833 | 0.948027 | 1.234503 |
 | MXFP8 | 0.071229 | 0.094774 | 0.035132 | 0.917225 | 0.979443 | 0.901653 | 2.643008 |
 | NVFP4 | 0.205124 | 0.223690 | 0.084436 | 0.768737 | 0.934769 | 0.796323 | 6.313444 |
-Statistics use 50,000 prompt-cluster BCa bootstrap resamples. Quantized-format pairwise tests use all 2^15 exact prompt-level sign permutations with Holm correction across the six pairs per metric.
 ## Prompt adherence and no-reference quality
@@ -90,6 +115,9 @@ Statistics use 50,000 prompt-cluster BCa bootstrap resamples. Quantized-format p
 | INT8 ConvRot | 41.762024 | 22.668381 | 0.288574 | 70.952457 | 0.458785 | 0.596355 | 4.369028 | 26.581604 |
 | MXFP8 | 41.587543 | 22.493693 | 0.289673 | 72.054489 | 0.463579 | 0.594426 | 4.395214 | 25.064392 |
 | NVFP4 | 41.326290 | 22.693707 | 0.285645 | 69.801792 | 0.479385 | 0.591738 | 4.430767 | 26.218567 |
 These models have different training biases, particularly on illustration and typography. They are reported independently and are never averaged into a single score.
@@ -102,6 +130,9 @@ These models have different training biases, particularly on illustration and ty
 | INT8 ConvRot | 6 | 0.666667 | 0.666667 | 0.133333 | 0.763667 | 4.67 |
 | MXFP8 | 6 | 0.611111 | 0.666667 | 0.157265 | 0.759152 | 4.50 |
 | NVFP4 | 6 | 0.611111 | 0.666667 | 0.152381 | 0.780622 | 4.67 |
 OCR is evaluated only on the six package/storefront/scientific-poster images per format. Exact target fraction is the share of requested phrases found verbatim after uppercase normalization.
@@ -124,13 +155,18 @@ For every float decoded image the analysis records clipping, gradient distributi
 | INT8 ConvRot | 13.623 | 12.417 | 0.356 | 15165.0 | 2.309 | 141.7 | 1944.8 | 72.0 |
 | MXFP8 | 33.244 | 31.929 | 0.345 | 14750.0 | 4.507 | 124.3 | 4142.8 | 69.0 |
 | NVFP4 | 26.391 | 24.925 | 0.345 | 14459.0 | 3.730 | 139.0 | 3679.3 | 71.0 |
 Latency summaries use 30 scored runs per format after three repeatability/warmup executions. GPU and system telemetry were sampled every 100 ms. Model-load and warmup records remain available in `runs.jsonl` and telemetry files but are excluded from steady-state medians.
 ## Comparison sheets
 - [Master contact sheet](comparison_sheets/contact_sheet_replicate0.png)
-- [`comparison_sheets/full/`](comparison_sheets/full/) — labeled five-format images
 - [`comparison_sheets/details/`](comparison_sheets/details/) — three automatically selected high-detail crops per pair
 - [`comparison_sheets/differences/`](comparison_sheets/differences/) — BF16-relative absolute-difference maps with a shared per-pair scale
@@ -207,12 +243,13 @@ OCR targets: AIR, WATER, ROOTS, LIGHT.
 ## Reproduction
 ```powershell
-.\run_benchmark.ps1 preflight
-.\run_benchmark.ps1 weights
-.\run_benchmark.ps1 generate
-.\run_benchmark.ps1 analyze
-.\run_benchmark.ps1 sheets
-.\run_benchmark.ps1 report
 ```
 The runner resumes only complete captures and rejects an existing result directory when the configuration hash differs. It never silently retries with different image settings.
@@ -224,12 +261,16 @@ The external capture sampler was also compared against ComfyUI's built-in KSampl
 2. BF16 is the original-checkpoint fidelity reference, not an external ground truth for semantic correctness.
 3. Pixel metrics become conservative when quantization changes composition rather than producing a local defect.
 4. No human preference test was requested; conclusions about aesthetics remain outside scope.
-5. FP8 and INT8 have accelerated Ada execution in this runtime. MXFP8 and NVFP4 do not, so only their storage and quality results are portable from this system.
 ## Primary sources
 - [Krea 2 Turbo model card and official inference settings](https://huggingface.co/krea/Krea-2-Turbo)
 - [Comfy-Org Krea 2 repackaged checkpoints](https://huggingface.co/Comfy-Org/Krea-2)
 - [LPIPS paper](https://arxiv.org/abs/1801.03924)
 - [DISTS paper](https://arxiv.org/abs/2004.07728)
 - [DINOv2 paper](https://arxiv.org/abs/2304.07193)
@@ -241,7 +282,7 @@ The external capture sampler was also compared against ComfyUI's built-in KSampl
 ## Raw artifact index
 - `manifest.json`: environment, dependency versions, checkpoint metadata, and hashes
-- `run_matrix.json`: all 150 preregistered scored runs
 - `runs.jsonl`: execution ledger including failures and timings
 - `captures/`: float decoded images, final latents, trajectories, PNGs, and per-run metadata
 - `metrics/`: all raw, summary, and inferential CSV/JSON tables

 - **Highest model fidelity:** BF16, because it is the unquantized reference checkpoint used for all paired comparisons.
 - **Best quantized BF16 fidelity by the preregistered primary endpoint (LPIPS-Alex):** INT8 ConvRot.
+- **Primary-endpoint separation from the next quantized format:** not statistically separated at α=0.05; treat the numerical ordering as a near-tie.
+- **Smallest checkpoint:** INT4 ConvRot W4A4.
 - **Ada performance warning:** MXFP8 and NVFP4 use fallback/dequantized matrix multiplication on this SM 8.9 GPU; their recorded speed must not be projected to Blackwell hardware.
+- **INT4 execution note:** ConvRot W4A4 is eligible for the native SM 8.x signed-INT4 MMA path in this comfy-kitchen runtime.
+- **GGUF execution note:** Q8_0 and Q4_K_M use the pinned ComfyUI-GGUF loader and its on-demand dequantized matrix-multiplication path, not ComfyUI's native safetensors quantization layouts.
 - Automated results establish original-model fidelity, prompt alignment, and measurable artifact behavior. They do not establish universal human aesthetic preference.
 ### Decision table
 | INT8 ConvRot | 12.566 | 0.041864 | 0.026796 | 0.983833 | 0.132571 | 41.157030 | 12.417 | 15165.0 | 1944.8 |
 | MXFP8 | 12.603 | 0.071229 | 0.035132 | 0.979443 | 0.201900 | 31.604820 | 31.929 | 14750.0 | 4142.8 |
 | NVFP4 | 7.147 | 0.205124 | 0.084436 | 0.934769 | 0.381361 | 20.600707 | 24.925 | 14459.0 | 3679.3 |
+| INT4 ConvRot W4A4 | 6.426 | 0.314728 | 0.132647 | 0.911258 | 0.490868 | 15.715704 | 9.537 | 14623.0 | 1440.8 |
+| GGUF Q8_0 | 12.765 | 0.042618 | 0.024559 | 0.990568 | 0.145688 | 44.813097 | 27.493 | 14267.0 | 3997.6 |
+| GGUF Q4_K_M | 6.972 | 0.153252 | 0.066291 | 0.954597 | 0.340616 | 22.973296 | 31.407 | 14333.0 | 4487.2 |
 The decision table is multidimensional by design. No weighted composite score is used.
+## Three-format extension conclusion
+- **Best fidelity among the three additions: GGUF Q8_0.** It records LPIPS 0.042618, DISTS 0.024559, DINO cosine 0.990568, and 44.81 dB reconstructed-weight SNR. It is also effectively tied with INT8 ConvRot on the primary endpoint (Holm-adjusted permutation p=0.9067), while leading INT8 on DISTS, DINO cosine, and weight SNR.
+- **Best minimum-size/speed option among the three: INT4 ConvRot W4A4.** It is 6.426 GiB and sampled in 9.537 s on this RTX 4060 Ti, but it has the largest quality loss: LPIPS 0.314728, final-latent relative L2 0.490868, and 15.72 dB weight SNR.
+- **Storage-quality compromise: GGUF Q4_K_M.** At 6.972 GiB it is only 8.5% larger than INT4 ConvRot, while reducing LPIPS by 51.3%. Its 31.407 s sampling time is 3.29× INT4's time because this Ada test uses ComfyUI-GGUF's dequantized execution path.
+- **Q8_0 costs size and Ada runtime for fidelity.** Its 12.765 GiB checkpoint is 1.99× the INT4 file and its 27.493 s sampling time is 2.88× INT4's, but it preserves the BF16 result far more closely on every paired fidelity endpoint.
+- The three extension formats ran in the same session, so their timing comparison is direct. Exact timing comparisons against the original five-format session remain caveated by the bridge test.
 ## Test system and immutable controls
 - GPU: `NVIDIA GeForce RTX 4060 Ti; driver 610.74; 16380 MiB; compute capability 8.9`
 - Sampler: euler; scheduler: simple; steps: 8; CFG: 1.0; denoise: 1.0
 - Shared text encoder: `qwen3vl_4b_bf16.safetensors`; shared VAE: `qwen_image_vae.safetensors`
 - Negative conditioning is a zeroed copy of positive conditioning. Prompt rewriting, LoRAs, previews, upscaling, and post-processing are disabled.
+- Each prompt/seed pair uses the same initial-noise tensor SHA-256 across all 8 formats.
 ## Checkpoint structure
+| Format | File | SHA-256 | GiB | Loader | Tensors | Stored dtypes/qtypes | Metadata |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| BF16 | krea2_turbo_bf16.safetensors | 78bbf8f4165eda19cea3cb06c78089221932a39e2eed8af9da741f942c47ffb3 | 24.478 | unet | 430 | BF16: 256<br>F32: 174 | none |
+| FP8 Scaled | krea2_turbo_fp8_scaled.safetensors | eb4dd8c612cfd10f64f25b057e6e6bbcb5737c94a7372177e456dbf7579502f1 | 12.239 | unet | 686 | BF16: 174<br>F32: 256<br>F8_E4M3: 256 | _quantization_metadata, format |
+| INT8 ConvRot | krea2_turbo_int8_convrot.safetensors | 8e4eeda70dd5037ab1ba2bef6b417f9f901e26093117cf397f741fc1fdaaf3f1 | 12.566 | unet | 878 | BF16: 76<br>F32: 354<br>I8: 224<br>U8: 224 | none |
+| MXFP8 | krea2_turbo_mxfp8.safetensors | 4c09131a442c3a01e95f934b43c76c1e691b9f683694a7b68d6fc707d5ecfce3 | 12.603 | unet | 686 | BF16: 174<br>F8_E4M3: 256<br>U8: 256 | _quantization_metadata, format |
+| NVFP4 | krea2_turbo_nvfp4.safetensors | 61527003b2d537055494d01bc8efe51d6e86e64192ba23e3721a5647231fe394 | 7.147 | unet | 942 | BF16: 174<br>F32: 256<br>F8_E4M3: 256<br>U8: 256 | _quantization_metadata, format |
+| INT4 ConvRot W4A4 | krea2_turbo_int4_convrot.safetensors | 4c74e8fc00d121aeb4f75cdf066b70d33f6a59e36f143b0bfd53d6467ffc13bd | 6.426 | unet | 686 | BF16: 174<br>F32: 256<br>I8: 256 | _quantization_metadata, converted_by |
+| GGUF Q8_0 | krea2_turbo-Q8_0.gguf | 1fa2da08a7a708827c2100d0af41c8371d79efd9f7c5490c23ebc65408536490 | 12.765 | gguf | 432 | F32: 167<br>Q8_0: 265 | general.architecture, general.file_type, general.quantization_version |
+| GGUF Q4_K_M | krea2_turbo-Q4_K_M.gguf | bc12f539de7a7a6ddf9bac17ec6e5bfecd42c3517190f491131d8464a1034b40 | 6.972 | gguf | 432 | F16: 1<br>F32: 167<br>Q4_K: 201<br>Q5_K: 63 | general.architecture, general.file_type, general.quantization_version |
 ### What the formats actually do
 - **INT8 ConvRot:** 224 row-wise INT8 linear weights with group-size-256 online/offline Hadamard rotation. Sensitive input/output, conditioning, and text-fusion components remain higher precision.
 - **MXFP8:** E4M3 values with E8M0 power-of-two block scales over 32-element blocks. It marks 256 linear layers; native fast multiplication requires SM 10.0 in this runtime.
 - **NVFP4:** packed 4-bit floating-point values with per-tensor and 16-element block scales. It marks 256 linear layers; native fast multiplication likewise requires SM 10.0.
+- **INT4 ConvRot W4A4:** 256 packed signed-INT4 linear weights with FP32 scales, ConvRot group size 256, quantization group size 64, and 174 BF16 tensors. ComfyUI 0.27 loads it through the native diffusion-model loader.
+- **GGUF Q8_0:** third-party GGUF representation loaded by ComfyUI-GGUF. Its exact tensor qtype distribution is recorded in the manifest and its dequantized logical weights are audited against BF16.
+- **GGUF Q4_K_M:** mixed K-quant GGUF representation using the same pinned loader. File size is not treated as proof of quality; reconstructed weights and matched outputs determine fidelity.
 ## Weight reconstruction fidelity
 | INT8 ConvRot | 430 | 224 | 0.000277 | 0.000387 | 0.008753 | 0.999961693 | 41.157 | 0.091442 |
 | MXFP8 | 430 | 256 | 0.000721 | 0.001163 | 0.026288 | 0.999654434 | 31.605 | 0.250000 |
 | NVFP4 | 430 | 256 | 0.002923 | 0.004129 | 0.093318 | 0.995646025 | 20.601 | 0.531250 |
+| INT4 ConvRot W4A4 | 430 | 256 | 0.005451 | 0.007245 | 0.163763 | 0.986854981 | 15.716 | 0.625000 |
+| GGUF Q8_0 | 430 | 263 | 0.000201 | 0.000254 | 0.005746 | 0.999983494 | 44.813 | 0.022461 |
+| GGUF Q4_K_M | 430 | 262 | 0.002456 | 0.003142 | 0.071013 | 0.997478661 | 22.973 | 0.144531 |
 Full per-parameter results, including p50/p95/p99/p99.9 error samples and subsystem grouping, are in [`metrics/weight_parameters_all.csv`](metrics/weight_parameters_all.csv).
 | INT8 ConvRot | 0.041864 | 0.050027 | 0.026796 | 0.956164 | 0.983833 | 0.948027 | 1.234503 |
 | MXFP8 | 0.071229 | 0.094774 | 0.035132 | 0.917225 | 0.979443 | 0.901653 | 2.643008 |
 | NVFP4 | 0.205124 | 0.223690 | 0.084436 | 0.768737 | 0.934769 | 0.796323 | 6.313444 |
+| INT4 ConvRot W4A4 | 0.314728 | 0.336843 | 0.132647 | 0.642767 | 0.911258 | 0.662332 | 9.150108 |
+| GGUF Q8_0 | 0.042618 | 0.055029 | 0.024559 | 0.952685 | 0.990568 | 0.944028 | 1.862765 |
+| GGUF Q4_K_M | 0.153252 | 0.188202 | 0.066291 | 0.798899 | 0.954597 | 0.825529 | 5.339750 |
+Statistics use 50,000 prompt-cluster BCa bootstrap resamples. Quantized-format pairwise tests use all 2^15 exact prompt-level sign permutations with Holm correction across 21 pairs per metric.
 ## Prompt adherence and no-reference quality
 | INT8 ConvRot | 41.762024 | 22.668381 | 0.288574 | 70.952457 | 0.458785 | 0.596355 | 4.369028 | 26.581604 |
 | MXFP8 | 41.587543 | 22.493693 | 0.289673 | 72.054489 | 0.463579 | 0.594426 | 4.395214 | 25.064392 |
 | NVFP4 | 41.326290 | 22.693707 | 0.285645 | 69.801792 | 0.479385 | 0.591738 | 4.430767 | 26.218567 |
+| INT4 ConvRot W4A4 | 40.899118 | 22.446506 | 0.286499 | 72.287605 | 0.481754 | 0.602120 | 4.407180 | 23.496765 |
+| GGUF Q8_0 | 41.225086 | 22.583970 | 0.286743 | 71.787231 | 0.462959 | 0.598738 | 4.274705 | 25.504883 |
+| GGUF Q4_K_M | 40.991884 | 22.502078 | 0.285156 | 71.055096 | 0.456792 | 0.583141 | 4.305879 | 25.181091 |
 These models have different training biases, particularly on illustration and typography. They are reported independently and are never averaged into a single score.
 | INT8 ConvRot | 6 | 0.666667 | 0.666667 | 0.133333 | 0.763667 | 4.67 |
 | MXFP8 | 6 | 0.611111 | 0.666667 | 0.157265 | 0.759152 | 4.50 |
 | NVFP4 | 6 | 0.611111 | 0.666667 | 0.152381 | 0.780622 | 4.67 |
+| INT4 ConvRot W4A4 | 6 | 0.722222 | 0.666667 | 0.113492 | 0.778992 | 4.67 |
+| GGUF Q8_0 | 6 | 0.611111 | 0.666667 | 0.195757 | 0.766976 | 4.50 |
+| GGUF Q4_K_M | 6 | 0.722222 | 0.833333 | 0.074311 | 0.791182 | 3.83 |
 OCR is evaluated only on the six package/storefront/scientific-poster images per format. Exact target fraction is the share of requested phrases found verbatim after uppercase normalization.
 | INT8 ConvRot | 13.623 | 12.417 | 0.356 | 15165.0 | 2.309 | 141.7 | 1944.8 | 72.0 |
 | MXFP8 | 33.244 | 31.929 | 0.345 | 14750.0 | 4.507 | 124.3 | 4142.8 | 69.0 |
 | NVFP4 | 26.391 | 24.925 | 0.345 | 14459.0 | 3.730 | 139.0 | 3679.3 | 71.0 |
+| INT4 ConvRot W4A4 | 10.919 | 9.537 | 0.347 | 14623.0 | 2.303 | 131.5 | 1440.8 | 71.0 |
+| GGUF Q8_0 | 29.344 | 27.493 | 0.802 | 14267.0 | 5.498 | 136.3 | 3997.6 | 72.0 |
+| GGUF Q4_K_M | 32.743 | 31.407 | 0.355 | 14333.0 | 2.314 | 136.4 | 4487.2 | 71.0 |
 Latency summaries use 30 scored runs per format after three repeatability/warmup executions. GPU and system telemetry were sampled every 100 ms. Model-load and warmup records remain available in `runs.jsonl` and telemetry files but are excluded from steady-state medians.
+The original five formats and the three extension formats were measured in separate sessions. Five unscored BF16 and INT8 ConvRot bridge repetitions quantify session drift; no timing is automatically rescaled.
+Bridge verdict: **keep cross-session timing comparisons caveated** using a 5% median-drift threshold.
 ## Comparison sheets
 - [Master contact sheet](comparison_sheets/contact_sheet_replicate0.png)
+- [`comparison_sheets/full/`](comparison_sheets/full/) — labeled 8-format images
 - [`comparison_sheets/details/`](comparison_sheets/details/) — three automatically selected high-detail crops per pair
 - [`comparison_sheets/differences/`](comparison_sheets/differences/) — BF16-relative absolute-difference maps with a shared per-pair scale
 ## Reproduction
 ```powershell
+.\run_benchmark.ps1 preflight -Config .\config_extended.json
+.\run_benchmark.ps1 weights -Config .\config_extended.json
+.\run_benchmark.ps1 generate -Config .\config_extended.json
+.\run_benchmark.ps1 analyze -Config .\config_extended.json
+.\run_benchmark.ps1 sheets -Config .\config_extended.json
+.\run_benchmark.ps1 report -Config .\config_extended.json
+.\run_benchmark.ps1 verify -Config .\config_extended.json
 ```
 The runner resumes only complete captures and rejects an existing result directory when the configuration hash differs. It never silently retries with different image settings.
 2. BF16 is the original-checkpoint fidelity reference, not an external ground truth for semantic correctness.
 3. Pixel metrics become conservative when quantization changes composition rather than producing a local defect.
 4. No human preference test was requested; conclusions about aesthetics remain outside scope.
+5. Performance is hardware- and loader-specific. Native safetensors quantization, ConvRot W4A4, and ComfyUI-GGUF follow different execution paths; storage and paired fidelity are more portable than timing.
+6. The extension adds three formats in a later session. Bridge runs quantify drift, but a future single-session eight-format rerun remains the strongest timing design.
 ## Primary sources
 - [Krea 2 Turbo model card and official inference settings](https://huggingface.co/krea/Krea-2-Turbo)
 - [Comfy-Org Krea 2 repackaged checkpoints](https://huggingface.co/Comfy-Org/Krea-2)
+- [Winnougan INT4 ConvRot Comfy models](https://huggingface.co/Winnougan/INT4-Convrot-Comfy-Models)
+- [vantagewithai Krea 2 Turbo GGUF](https://huggingface.co/vantagewithai/Krea-2-Turbo-GGUF)
+- [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF)
 - [LPIPS paper](https://arxiv.org/abs/1801.03924)
 - [DISTS paper](https://arxiv.org/abs/2004.07728)
 - [DINOv2 paper](https://arxiv.org/abs/2304.07193)
 ## Raw artifact index
 - `manifest.json`: environment, dependency versions, checkpoint metadata, and hashes
+- `run_matrix.json`: all 240 preregistered scored runs
 - `runs.jsonl`: execution ledger including failures and timings
 - `captures/`: float decoded images, final latents, trajectories, PNGs, and per-run metadata
 - `metrics/`: all raw, summary, and inferential CSV/JSON tables

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 0.9030529241644532,0.14129167795181274,nvfp4,0.2666015625,0.1801292896270752,0.19220229983329773,0.7764748930931091,0.6666666666666666,0.15018315018315018,0.8023732706904412,5,NORTHSTAR | LAB | NORTHSTAR | SERIES 08 | 250 ML.,0.9332791912992493,48.05664825439453,22.908214569091797,p03_package_text,49.88079833984375,0.4903087913990021,74.94251251220703,6.949644275706244,0.6673346161842346,1,p03_package_text__r1__nvfp4
 0.9999999948659285,0.0,bf16,0.316162109375,0.0,0.0,1.0,0.3333333333333333,0.19999999999999998,0.7743320679354977,5,CAFE | TTH STREET | LUMEN | OPEN 24 HOURS | FRICE,1.000000064706256,48.83467483520508,25.028823852539062,p04_storefront_text,30.48626708984375,0.30830830335617065,64.89874267578125,3.4536950459197557,0.4745337665081024,0,p04_storefront_text__r0__bf16
 0.920164121881574,0.06548196077346802,fp8_scaled,0.310546875,0.17221540212631226,0.22081126272678375,0.8530095815658569,0.3333333333333333,0.19999999999999998,0.7717581937710444,4,CAFE | 7TH STRET | LUMEN | OPEN 24 HOURS,0.974226961250514,47.80848693847656,24.920520782470703,p04_storefront_text,28.04962158203125,0.3129104673862457,63.61717987060547,3.610189621693244,0.4706093966960907,0,p04_storefront_text__r0__fp8_scaled
 0.976874377909674,0.04109472036361694,int8_convrot,0.31640625,0.09424148499965668,0.1306486427783966,0.9186582565307617,0.6666666666666666,0.16666666666666666,0.7246952406307319,5,CAFE | 7TH STREET | LUMEN | OPEN 24 HOURS | BADE,0.9779841574182704,48.35041809082031,25.10140609741211,p04_storefront_text,30.83538818359375,0.29667970538139343,63.180686950683594,3.4942921914786353,0.4730197787284851,0,p04_storefront_text__r0__int8_convrot
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 0.9213713841245468,0.08788132667541504,nvfp4,0.31005859375,0.2594889998435974,0.31399184465408325,0.7615189552307129,0.3333333333333333,0.2333333333333333,0.7568550336219015,4,CAFE | THSTREET | OPEN 24 HOURS | LUMEN,0.9581969824152035,48.59613800048828,24.77184295654297,p04_storefront_text,25.12835693359375,0.31348106265068054,63.918094635009766,3.538800388887889,0.48535284399986267,0,p04_storefront_text__r0__nvfp4
 1.000000020971539,0.0,bf16,0.316162109375,0.0,0.0,1.0,0.0,0.4282051282051282,0.7456174101418649,7,CAFE | 7TH | OPEN | LUMEN | STREET | 24 HOURS | 2888,1.0000000072387742,47.19422149658203,24.94066619873047,p04_storefront_text,31.87664794921875,0.3284153938293457,64.81311798095703,3.5711249611826905,0.47474321722984314,1,p04_storefront_text__r1__bf16
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 0.9392200687937964,0.08577489852905273,nvfp4,0.303466796875,0.23561078310012817,0.2940053939819336,0.7728787660598755,0.0,0.4282051282051282,0.7399276736304332,7,CAFE | OPEN | 7TH | LUMEN | STREET | 24 HOURS | TMN,0.9598965017402027,48.532894134521484,24.330673217773438,p04_storefront_text,32.00360107421875,0.3182346224784851,63.754791259765625,3.3403983826152035,0.48185089230537415,1,p04_storefront_text__r1__nvfp4
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 0.9912012291432362,0.041532814502716064,fp8_scaled,0.2471923828125,0.07020322978496552,0.0975879356265068,0.8799111843109131,nan,nan,nan,0,,0.9913045742548159,40.612815856933594,22.938621520996094,p05_architecture,36.16009521484375,0.48579710721969604,73.4522933959961,3.6412093611183756,0.6183503866195679,0,p05_architecture__r0__fp8_scaled
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 0.9077013373085625,0.08166646957397461,mxfp8,0.240966796875,0.2517963647842407,0.2619633674621582,0.6970457434654236,nan,nan,nan,0,,0.9507209782767104,40.15892791748047,22.3956298828125,p05_architecture,37.84576416015625,0.48338621854782104,73.25955963134766,3.863177000179025,0.6241349577903748,0,p05_architecture__r0__mxfp8
 0.8488111546955661,0.12398791313171387,nvfp4,0.243408203125,0.3587673306465149,0.37366151809692383,0.4998057782649994,nan,nan,nan,0,,0.9218407505597557,39.71263122558594,22.537628173828125,p05_architecture,37.73846435546875,0.5018033385276794,72.14484405517578,3.7231210563601866,0.6285812854766846,0,p05_architecture__r0__nvfp4
 1.0000000550688337,0.0,bf16,0.2427978515625,0.0,0.0,1.0,nan,nan,nan,0,,1.0000000007058871,42.150962829589844,22.285537719726562,p05_architecture,30.02703857421875,0.49331745505332947,73.40734100341797,4.0550387450746355,0.6177598834037781,1,p05_architecture__r1__bf16
 0.9960098297847586,0.02749866247177124,fp8_scaled,0.23681640625,0.04328903183341026,0.05444515123963356,0.954364538192749,nan,nan,nan,0,,0.9937050751609988,41.86296463012695,22.169111251831055,p05_architecture,32.89178466796875,0.4959002733230591,73.58293151855469,4.30788012769682,0.6271457076072693,1,p05_architecture__r1__fp8_scaled
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 0.9841958838200647,0.05290985107421875,nvfp4,0.240234375,0.13765178620815277,0.1653432995080948,0.8755872249603271,nan,nan,nan,0,,0.9884329099961828,41.789215087890625,22.095829010009766,p05_architecture,31.31854248046875,0.49968239665031433,72.58686065673828,4.472105798085079,0.6280539035797119,1,p05_architecture__r1__nvfp4
 0.9999999453571304,0.0,bf16,0.3447265625,0.0,0.0,1.0,nan,nan,nan,0,,0.9999999798592769,42.65753173828125,25.29480743408203,p06_reflections,21.45318603515625,0.520759105682373,72.48316955566406,3.756700752908876,0.6872645020484924,0,p06_reflections__r0__bf16
 0.9826166065174593,0.06523740291595459,fp8_scaled,0.353271484375,0.15852975845336914,0.19457699358463287,0.7442535758018494,nan,nan,nan,0,,0.9542502306799163,42.52729034423828,25.59395980834961,p06_reflections,19.07440185546875,0.5197836756706238,73.85685729980469,3.7501599521821825,0.695905327796936,0,p06_reflections__r0__fp8_scaled
 0.9889457657469354,0.037947773933410645,int8_convrot,0.345703125,0.06044542416930199,0.0803600400686264,0.9069937467575073,nan,nan,nan,0,,0.9776864700111072,43.28050994873047,25.285961151123047,p06_reflections,19.39923095703125,0.5130378603935242,73.38409423828125,3.7856549202555185,0.6705564260482788,0,p06_reflections__r0__int8_convrot
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 0.9962894286657721,0.017097890377044678,int8_convrot,0.34619140625,0.025482477620244026,0.04077092558145523,0.966291069984436,nan,nan,nan,0,,0.9936014835580084,42.110008239746094,25.15778160095215,p06_reflections,29.92181396484375,0.4901779592037201,71.92987823486328,3.7501502327924934,0.6685236096382141,1,p06_reflections__r1__int8_convrot
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metrics/image_advanced_pairwise_statistics.csv CHANGED Viewed

@@ -1,91 +1,316 @@
 left_format,mean_paired_difference_left_minus_right,median_paired_difference_left_minus_right,metric,permutation_p_holm,permutation_p_raw,rank_biserial,right_format,standardized_paired_effect
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 fp8_scaled,-0.03333333333333333,0.0,ocr_recognized_line_count,1.0,1.0,-1.0,mxfp8,-0.1825741858350554
 fp8_scaled,-0.06666666666666667,0.0,ocr_recognized_line_count,1.0,0.5000152583234154,-1.0,nvfp4,-0.26276913640612176
 int8_convrot,0.03333333333333333,0.0,ocr_recognized_line_count,1.0,1.0,1.0,mxfp8,0.1825741858350554
 int8_convrot,0.0,0.0,ocr_recognized_line_count,1.0,1.0,0.0,nvfp4,0.0
 mxfp8,-0.03333333333333333,0.0,ocr_recognized_line_count,1.0,1.0,-1.0,nvfp4,-0.1825741858350554
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 fp8_scaled,-0.11425679524739583,-0.16639137268066406,openclip_vith14_prompt_score,1.0,0.5811285055998047,-0.4666666666666667,mxfp8,-0.1175029448834697
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 int8_convrot,-0.06995760599772136,-0.06518363952636719,openclip_vith14_prompt_score,1.0,0.7141200524886325,-0.3333333333333333,mxfp8,-0.07166046522228162
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 fp8_scaled,-0.049491500854492186,-0.03954887390136719,pickscore_v1,1.0,0.272666239433611,-0.2,int8_convrot,-0.25110250344070284
 fp8_scaled,0.02095788319905599,0.03464221954345703,pickscore_v1,1.0,0.6771949098233087,0.2,mxfp8,0.08362997346518622
 fp8_scaled,0.045140711466471355,0.07201957702636719,pickscore_v1,1.0,0.5706918123836553,0.06666666666666667,nvfp4,0.12300056623208194
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 int8_convrot,0.09463221232096354,0.07480716705322266,pickscore_v1,1.0,0.2317739326802771,0.3333333333333333,nvfp4,0.27143959881653396
 mxfp8,0.024182828267415364,-0.041229248046875,pickscore_v1,1.0,0.7556226921785835,-0.06666666666666667,nvfp4,0.07630940464868596
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 fp8_scaled,0.17278645833333334,0.330322265625,pyiqa_brisque,1.0,0.7188806493942446,-0.06666666666666667,mxfp8,0.06580386301806593
 fp8_scaled,0.27162272135416665,-0.1939697265625,pyiqa_brisque,1.0,0.788641704049559,-0.06666666666666667,nvfp4,0.06232722361543333
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 int8_convrot,0.8938028971354167,-0.150146484375,pyiqa_brisque,1.0,0.3659861454423388,-0.06666666666666667,nvfp4,0.214471855191788
 mxfp8,0.09883626302083333,-0.3721923828125,pyiqa_brisque,1.0,0.924379749153163,-0.3333333333333333,nvfp4,0.023682418958543193
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 fp8_scaled,0.002706428368886312,0.0040501803159713745,pyiqa_maniqa,1.0,0.47242820958833043,0.4666666666666667,nvfp4,0.1517935916754868
 int8_convrot,-0.0017680535713831584,-0.0009584426879882812,pyiqa_maniqa,1.0,0.47071927736580305,-0.06666666666666667,mxfp8,-0.16482314963949696
 int8_convrot,-0.0007274250189463297,0.00337432324886322,pyiqa_maniqa,1.0,0.8340504745338582,0.06666666666666667,nvfp4,-0.03963539834195116
 mxfp8,0.0010406285524368285,0.0026386380195617676,pyiqa_maniqa,1.0,0.7639842534102352,0.3333333333333333,nvfp4,0.05976649432839801
 fp8_scaled,0.37436644236246747,0.13973617553710938,pyiqa_musiq,1.0,0.3817327352070555,-0.06666666666666667,int8_convrot,0.23533355085896504
 fp8_scaled,0.0039046605428059897,-0.024715423583984375,pyiqa_musiq,1.0,0.983521010711343,0.3333333333333333,mxfp8,0.004166622709297343
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 int8_convrot,-0.37046178181966144,-0.07075119018554688,pyiqa_musiq,1.0,0.3733101406817419,0.06666666666666667,mxfp8,-0.2292968830944883
 int8_convrot,0.2945172627766927,0.3830909729003906,pyiqa_musiq,1.0,0.4566205865299521,0.2,nvfp4,0.1347315776861467
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 fp8_scaled,-0.027961742213209552,-0.024534603728256332,pyiqa_niqe,1.0,0.6905001678415575,0.2,int8_convrot,-0.09136306375730678
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 fp8_scaled,0.006521797569435206,-0.009633851975517471,pyiqa_niqe,1.0,0.9082059263328145,0.2,nvfp4,0.02178360754259599
 int8_convrot,0.1555397593283496,-0.007483116785064192,pyiqa_niqe,1.0,0.32600933809393023,-0.06666666666666667,mxfp8,0.2609774609248157
 int8_convrot,0.034483539782644755,-0.02433628792406095,pyiqa_niqe,1.0,0.6522322927156764,0.06666666666666667,nvfp4,0.10493922698834962
-mxfp8,-0.12105621954570485,-0.04398211131402752,pyiqa_niqe,0.9235863163355611,0.18471726326711221,-0.3333333333333333,nvfp4,-0.26430613140655984
-fp8_scaled,0.005892618497212728,0.005368947982788086,pyiqa_topiq_nr,0.23085843327535172,0.04617168665507034,0.4666666666666667,int8_convrot,0.45399488692550544
 fp8_scaled,0.0011031260093053183,0.0016209036111831665,pyiqa_topiq_nr,1.0,0.6416735329122036,0.2,mxfp8,0.09975166145473922
-fp8_scaled,0.006982232133547465,0.006140410900115967,pyiqa_topiq_nr,0.24230217583691904,0.06057554395922976,0.3333333333333333,nvfp4,0.3731290591848374
-int8_convrot,-0.00478949248790741,-0.0034291744232177734,pyiqa_topiq_nr,0.1642405932436144,0.027373432207269066,-0.6,mxfp8,-0.44274968618655247
 int8_convrot,0.0010896136363347372,0.0021250545978546143,pyiqa_topiq_nr,1.0,0.6680399157740547,-0.06666666666666667,nvfp4,0.07563347535753298
-mxfp8,0.005879106124242147,0.0072859227657318115,pyiqa_topiq_nr,0.24230217583691904,0.062467576062742225,0.4666666666666667,nvfp4,0.36413588374509676

 left_format,mean_paired_difference_left_minus_right,median_paired_difference_left_minus_right,metric,permutation_p_holm,permutation_p_raw,rank_biserial,right_format,standardized_paired_effect
+fp8_scaled,-0.015484754901897092,-0.007982871271661973,dinov2_large_image_cosine_to_bf16,0.41121181604565293,0.08224236320913059,-0.6,int8_convrot,-0.41419720806165866
+fp8_scaled,-0.005849007816441283,-0.003389604275672198,dinov2_large_image_cosine_to_bf16,0.4932100460801367,0.2535017852238396,-0.4666666666666667,mxfp8,-0.1792828595636529
+fp8_scaled,0.03808280194605066,0.01561802702047671,dinov2_large_image_cosine_to_bf16,0.011901492264030028,0.0011901492264030028,0.7333333333333333,nvfp4,0.767488341787752
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metrics/image_advanced_summary.csv CHANGED Viewed

@@ -4,73 +4,118 @@ fp8_scaled,0.9990380046966528,0.9592263163287699,0.9745078495123376,0.9348029715
 int8_convrot,0.9994426207438029,0.9747110712306669,0.9840584932999488,0.9616270341773688,0.9838329627814222,0.9938522515155327,0.9637905948588256,dinov2_large_image_cosine_to_bf16,0.8697761881740599,30,0.9625561276707824,0.9944581270668518,0.02761079659221253
 mxfp8,0.9992130391924932,0.9650753241452109,0.9774617812160433,0.946008471820417,0.9794431846076316,0.9881060875147536,0.9644349477737532,dinov2_large_image_cosine_to_bf16,0.8435983265842217,30,0.9578004265676212,0.9892633205852963,0.03928833421484908
 nvfp4,0.9927072006236084,0.921143514382719,0.9436932438894632,0.8901975713664323,0.9347693606993568,0.9708130087272056,0.8923405128580253,dinov2_large_image_cosine_to_bf16,0.7742967238004093,30,0.8702379679626541,0.9744959468668348,0.06233988811743947
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,dists,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.15055304765701294,0.051304304599761964,0.06997998300674296,0.03981140081664646,0.042738109827041626,0.05922994017601013,0.03169947862625122,dists,0.017882049083709717,30,0.0299847275018692,0.06542082130908966,0.031550285048114246
-int8_convrot,0.11818915605545044,0.03466375072797139,0.05037057162841268,0.02522285633211506,0.026795655488967896,0.03763192892074585,0.02108156681060791,dists,0.003576338291168213,30,0.01743854582309723,0.04030798375606537,0.02801484933406217
-mxfp8,0.1401481032371521,0.04750467340151469,0.06710388461498897,0.03490696323486823,0.035131752490997314,0.0532895028591156,0.030532002449035645,dists,0.004149675369262695,30,0.029238015413284302,0.05851832032203674,0.033330181739859764
-nvfp4,0.19701147079467773,0.08801797827084859,0.10236987979622217,0.07657417343594851,0.08443576097488403,0.10016334056854248,0.06442761421203613,dists,0.03400731086730957,30,0.061743080615997314,0.11066558957099915,0.03621241900517073
-bf16,0.3447265625,0.27633056640625,0.2972054846492699,0.25103106429665073,0.2877197265625,0.308349609375,0.24310302734375,hpsv2_1,0.171142578125,30,0.244293212890625,0.31591796875,0.04651941224206469
-fp8_scaled,0.353271484375,0.27591552734375,0.29742431640625,0.2517903645833333,0.2857666015625,0.29736328125,0.2432861328125,hpsv2_1,0.177978515625,30,0.2413330078125,0.303955078125,0.04584655460162336
-int8_convrot,0.34619140625,0.27720540364583335,0.2977213541666667,0.25203241372747565,0.28857421875,0.3095703125,0.2440185546875,hpsv2_1,0.17333984375,30,0.24517822265625,0.3150634765625,0.04610534438911367
-mxfp8,0.35205078125,0.276611328125,0.29786783854166665,0.2513346354166667,0.2896728515625,0.3046875,0.23974609375,hpsv2_1,0.171875,30,0.239959716796875,0.3074951171875,0.04670293512810871
-nvfp4,0.348876953125,0.2723795572916667,0.2933430989583333,0.24718153121483463,0.28564453125,0.2987060546875,0.236083984375,hpsv2_1,0.16455078125,30,0.23712158203125,0.3023681640625,0.046515017925069436
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,lpips_alex,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.3820990324020386,0.1264743079120914,0.1877545793599469,0.08230273258652485,0.09370071068406105,0.17221540212631226,0.04312717542052269,lpips_alex,0.021495753899216652,30,0.042709083296358585,0.17643478512763977,0.10711371911167711
-int8_convrot,0.28394433856010437,0.07244204020438096,0.10806849764299715,0.047381019595556975,0.04186439700424671,0.08600236475467682,0.02455641608685255,lpips_alex,0.005779309663921595,30,0.023480938747525215,0.09854498319327831,0.07215048423581227
-mxfp8,0.3678184747695923,0.10497030741535127,0.1552691639898266,0.0678482077363408,0.07122896611690521,0.1423274278640747,0.03228048235177994,lpips_alex,0.0031711203046143055,30,0.03099331259727478,0.15937044098973274,0.09732756936551985
-nvfp4,0.44609522819519043,0.21921894767632086,0.2615477018729113,0.17165302118414694,0.20512405037879944,0.2915562391281128,0.14014990627765656,lpips_alex,0.024270767346024513,30,0.13827631622552872,0.305231548845768,0.11252626171472946
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,lpips_vgg,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.3665781617164612,0.14539796921114126,0.2017187072321804,0.10243147794507858,0.09543363004922867,0.18791768699884415,0.05986759997904301,lpips_vgg,0.0344182550907135,30,0.05911336001008749,0.21097444370388985,0.10244581089377869
-int8_convrot,0.30687659978866577,0.08575619366019964,0.12147646286938708,0.0602590792585133,0.050026947632431984,0.1094667911529541,0.034907590597867966,lpips_vgg,0.012240605428814888,30,0.034275008365511894,0.1303403116762638,0.07150329391836473
-mxfp8,0.35648101568222046,0.12272146384542187,0.17063295646789087,0.08646002345702329,0.0947742909193039,0.1796317845582962,0.05934765189886093,lpips_vgg,0.014053110964596272,30,0.0589706227183342,0.19296270608901978,0.09079705969066877
-nvfp4,0.44362935423851013,0.24042122587561607,0.2806764261433434,0.19756900133845234,0.2236902341246605,0.31399184465408325,0.18480853736400604,lpips_vgg,0.07175146043300629,30,0.1702096089720726,0.32828274369239807,0.10108864041707974
 bf16,1.0,1.0,1.0,1.0,1.0,1.0,1.0,ms_ssim,1.0,30,1.0,1.0,0.0
-fp8_scaled,0.9930009841918945,0.8483239730199178,0.9071980926196603,0.7530740791013427,0.869319498538971,0.9625673294067383,0.8052575290203094,ms_ssim,0.43679410219192505,30,0.7964089959859848,0.9683683663606644,0.15242192268740185
-int8_convrot,0.9970208406448364,0.9166152695814769,0.9497582722083358,0.854960209532095,0.9561639130115509,0.9839557409286499,0.9065420627593994,ms_ssim,0.5410804748535156,30,0.8975338190793991,0.9885507076978683,0.10305741897618577
-mxfp8,0.9977141618728638,0.8777395576238632,0.9255808369769342,0.7929677837822666,0.9172250628471375,0.9839997291564941,0.8452204763889313,ms_ssim,0.44286975264549255,30,0.825881615281105,0.9876376092433929,0.13564754304482293
-nvfp4,0.9860314130783081,0.7496655414501826,0.8180352064573037,0.6765382174925091,0.7687365114688873,0.8618973195552826,0.6211484670639038,ms_ssim,0.42906951904296875,30,0.5945302695035934,0.8774771094322205,0.16538902192354002
 bf16,7.0,0.9333333333333333,2.1333333333333333,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.9640446187314116
 fp8_scaled,6.0,0.8666666666666667,1.8666666666666667,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.7952683659406263
 int8_convrot,7.0,0.9333333333333333,2.1333333333333333,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.9640446187314116
 mxfp8,7.0,0.9,2.0,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.9000907419347655
-nvfp4,7.0,0.9333333333333333,2.1333333333333333,0.26666666666666666,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.9640446187314111
-bf16,1.000000125266627,0.9999999940734259,1.0000000143508354,0.999999969481518,0.9999999923568554,1.0000000049637345,0.9999999616307156,openclip_vith14_image_cosine_to_bf16,0.9999998769699886,30,0.9999999471789609,1.0000000300142222,6.385695605784303e-08
-fp8_scaled,0.9965124998783443,0.9791456502532384,0.9852383903432129,0.969787980074175,0.9863771911451533,0.989969561094228,0.974226961250514,openclip_vith14_image_cosine_to_bf16,0.9313569096429853,30,0.9731082294139901,0.9910437253915281,0.016882672154409112
-int8_convrot,0.9984301021934913,0.988199698017437,0.9920902336715357,0.981390518170262,0.993389964999944,0.9951276246734035,0.9839241501162583,openclip_vith14_image_cosine_to_bf16,0.940550418707031,30,0.9818777282732937,0.9964521522736833,0.012568302358385589
-mxfp8,0.9992730458008162,0.9819962665894467,0.9873909983996001,0.9733427601085388,0.9870062383489837,0.9896046824868459,0.9819852106407714,openclip_vith14_image_cosine_to_bf16,0.9318314106031923,30,0.9805235806302957,0.9913865440104338,0.01599301731225812
-nvfp4,0.9912252998774632,0.958928857737406,0.9665651120330562,0.9492250731830213,0.9602555988555297,0.9663282091009741,0.9525271345988313,openclip_vith14_image_cosine_to_bf16,0.890382264515351,30,0.9495445747135758,0.9763968999286123,0.023856647902892405
-bf16,51.275699615478516,41.73448168436686,44.42708774052202,39.21818840172116,41.32732963562012,44.22158622741699,37.6806755065918,openclip_vith14_prompt_score,32.6300048828125,30,37.64154624938965,46.55621528625488,5.296917473155288
-fp8_scaled,51.483497619628906,41.69884287516276,44.175413374139744,39.321040613926996,41.257368087768555,44.24574851989746,37.25627899169922,openclip_vith14_prompt_score,34.211238861083984,30,37.541443824768066,45.91065788269043,4.960724074952656
-int8_convrot,51.184898376464844,41.74314206441243,44.30166183808888,39.21681587194917,41.76202392578125,44.18941116333008,37.436418533325195,openclip_vith14_prompt_score,32.58320236206055,30,37.313395500183105,46.82499980926514,5.236417859366287
-mxfp8,51.27956008911133,41.81309967041015,44.49093627687697,39.253945632825335,41.58754348754883,44.63146209716797,37.887760162353516,openclip_vith14_prompt_score,32.4578857421875,30,37.85694694519043,46.197611808776855,5.356400715433708
-nvfp4,49.52870178222656,41.10029900868734,43.509801951778634,38.56854318892471,41.326290130615234,43.966217041015625,37.42136764526367,openclip_vith14_prompt_score,28.00858497619629,30,37.34918022155762,44.098819732666016,5.1095241436151
-bf16,25.29480743408203,22.62093989054362,23.345439190839738,21.93989000652878,22.496188163757324,23.060813903808594,21.685227394104004,pickscore_v1,19.95904541015625,30,21.673014163970947,23.276992797851562,1.4286343462104703
-fp8_scaled,25.59395980834961,22.57025864919027,23.30633104335041,21.901017072309404,22.677390098571777,23.034337997436523,21.571516036987305,pickscore_v1,19.942903518676758,30,21.533616065979004,23.21449089050293,1.4147090190680083
-int8_convrot,25.285961151123047,22.61975015004476,23.32571385393106,21.93969218864685,22.668380737304688,23.17673683166504,21.630441665649414,pickscore_v1,20.049015045166016,30,21.60701560974121,23.310662746429443,1.4064606063365444
-mxfp8,25.251150131225586,22.549300765991212,23.270551134273024,21.887638148828422,22.493693351745605,22.990985870361328,21.66012191772461,pickscore_v1,19.957420349121094,30,21.716032028198242,23.318511486053467,1.3873977056236133
-nvfp4,25.195621490478516,22.525117937723795,23.220734089159098,21.75826472230953,22.693706512451172,23.060690879821777,21.559537887573242,pickscore_v1,18.805866241455078,30,21.60318946838379,23.374277591705322,1.4798059498521416
-bf16,92.47869873046875,28.90343017578125,43.14664930562907,21.619382520562297,25.84674072265625,30.82025146484375,14.27899169921875,pyiqa_brisque,5.27423095703125,30,15.6673583984375,31.696044921875,20.044888654219015
-fp8_scaled,82.77606201171875,28.141845703125,40.74609957307949,20.75883343600875,25.12017822265625,32.8299560546875,11.59588623046875,pyiqa_brisque,4.93988037109375,30,13.748504638671875,33.50335693359375,19.201905236741815
-int8_convrot,84.04071044921875,28.76402587890625,41.33560086773508,21.676218296946633,26.58160400390625,32.2066650390625,14.94268798828125,pyiqa_brisque,5.02484130859375,30,16.05682373046875,32.88446044921875,18.782114217428536
-mxfp8,83.58953857421875,27.969059244791666,40.21498592673917,20.816170280927004,25.06439208984375,32.1849365234375,12.82952880859375,pyiqa_brisque,4.67498779296875,30,14.3382568359375,33.780609130859375,18.67648027263219
-nvfp4,78.13275146484375,27.870222981770834,38.289785966131944,21.21646564335701,26.21856689453125,30.67724609375,15.65789794921875,pyiqa_brisque,5.89056396484375,30,16.7606201171875,31.83233642578125,16.6439481517059
-bf16,0.5886019468307495,0.4486085921525955,0.48465854772906997,0.4071188788192645,0.46450284123420715,0.5032989382743835,0.38209348917007446,pyiqa_maniqa,0.30211153626441956,30,0.3840566948056221,0.5086005926132202,0.07910948523631957
-fp8_scaled,0.5733467936515808,0.45054213603337606,0.48668862471845076,0.4089012038354346,0.460313081741333,0.5022063255310059,0.3907573074102402,pyiqa_maniqa,0.3129104673862457,30,0.39040523022413254,0.5153893381357193,0.07805641661014472
-int8_convrot,0.5910975933074951,0.4471082826455434,0.4847267089377158,0.4053782917818752,0.4587847888469696,0.503241240978241,0.3857790529727936,pyiqa_maniqa,0.29667970538139343,30,0.3857003524899483,0.506202444434166,0.08083502706969084
-mxfp8,0.5723727941513062,0.4488763362169266,0.4849162817304722,0.4065311052211416,0.4635789692401886,0.506023108959198,0.3849424421787262,pyiqa_maniqa,0.302993506193161,30,0.38602184504270554,0.514892190694809,0.07931189558985263
-nvfp4,0.5702823996543884,0.44783570766448977,0.48424333563812627,0.40524266250324703,0.4793853163719177,0.5045722424983978,0.3713224530220032,pyiqa_maniqa,0.3072735071182251,30,0.3742770627140999,0.5077926069498062,0.07980009796442984
-bf16,77.23944854736328,68.20770530700683,71.81356808127629,57.47926862748922,71.56268310546875,73.33185577392578,69.0898323059082,pyiqa_musiq,28.860599517822266,30,68.82169532775879,73.8019905090332,11.43809754922697
-fp8_scaled,76.8619384765625,68.2199229558309,71.81256843124491,58.028811925274454,72.23281860351562,73.51761245727539,68.56974029541016,pyiqa_musiq,29.581417083740234,30,68.27790451049805,73.6502914428711,11.391187822474997
-int8_convrot,77.40489196777344,67.84555651346842,71.52411380008797,57.41404735515897,70.95245742797852,73.20479202270508,68.89421844482422,pyiqa_musiq,29.176006317138672,30,68.67657470703125,73.37248611450195,11.501212592171361
-mxfp8,76.84375762939453,68.21601829528808,71.87900613318287,57.36222302221397,72.05448913574219,72.9996452331543,69.21273803710938,pyiqa_musiq,28.116840362548828,30,69.35589027404785,73.52084159851074,11.695819942831415
-nvfp4,76.9495620727539,67.55103925069173,71.02799651745434,58.461892794656144,69.80179214477539,72.78083038330078,66.76284790039062,pyiqa_musiq,28.360130310058594,30,66.79618263244629,72.83377075195312,10.74531510096879
-bf16,11.766508692323699,5.2023862857583225,6.871134352669223,4.348583078119167,4.279002139260847,4.886892248269733,3.81730031543237,pyiqa_niqe,3.22929132562609,30,3.820825286903663,5.16346387413795,2.34490294456827
-fp8_scaled,11.69740062317578,5.271203360727984,6.915032676465849,4.419324265274454,4.447991296029056,5.187684336685193,3.8414335189670723,pyiqa_niqe,3.269988810959199,30,3.8046694760249737,5.233510314117815,2.312809300046681
-int8_convrot,12.175289572146195,5.299165102941194,7.064210525970279,4.400212296844108,4.369027962732032,5.083710502406942,3.7679025765240057,pyiqa_niqe,3.2159665728829796,30,3.7590264046582496,5.258197489205607,2.468737551280756
-mxfp8,10.800768208357503,5.143625343612843,6.525350247772099,4.383189949406326,4.395213866557401,5.169401275111138,3.8833719283203068,pyiqa_niqe,3.1744788871393754,30,3.8754448711764953,5.236629870264908,2.032163639354589
-nvfp4,11.706782729018412,5.264681563158549,6.862121249913565,4.4311118990380205,4.430766531945498,4.980185472286554,3.937436269506489,pyiqa_niqe,3.3403983826152035,30,3.9359129981531145,5.242094593319523,2.251213225292878
-bf16,0.7204250693321228,0.5733892023563385,0.6134785702006211,0.4981412006093309,0.5969395935535431,0.6177598834037781,0.5419432520866394,pyiqa_topiq_nr,0.2532931864261627,30,0.5382301807403564,0.6446931958198547,0.10809375919371231
-fp8_scaled,0.7197762131690979,0.5781486650307973,0.6167795460935875,0.5030784513985418,0.5981931686401367,0.652131199836731,0.5500116944313049,pyiqa_topiq_nr,0.25142282247543335,30,0.5435057431459427,0.6522476524114609,0.1063514700617778
-int8_convrot,0.7242098450660706,0.5722560465335846,0.612129447452098,0.49761992499191154,0.5963552296161652,0.6225141882896423,0.5320471525192261,pyiqa_topiq_nr,0.25106868147850037,30,0.5285522937774658,0.6445451378822327,0.10811301007703494
-mxfp8,0.7195144891738892,0.577045539021492,0.618339008594615,0.5008409022300009,0.5944256484508514,0.6503648161888123,0.538679301738739,pyiqa_topiq_nr,0.25287699699401855,30,0.5407610237598419,0.6643411815166473,0.10967850289783901
-nvfp4,0.7111228108406067,0.5711664328972499,0.6114363426910736,0.4990512117344318,0.5917383432388306,0.6285812854766846,0.5306038856506348,pyiqa_topiq_nr,0.2595398724079132,30,0.5260344743728638,0.6470286250114441,0.10660456934636497

 int8_convrot,0.9994426207438029,0.9747110712306669,0.9840584932999488,0.9616270341773688,0.9838329627814222,0.9938522515155327,0.9637905948588256,dinov2_large_image_cosine_to_bf16,0.8697761881740599,30,0.9625561276707824,0.9944581270668518,0.02761079659221253
 mxfp8,0.9992130391924932,0.9650753241452109,0.9774617812160433,0.946008471820417,0.9794431846076316,0.9881060875147536,0.9644349477737532,dinov2_large_image_cosine_to_bf16,0.8435983265842217,30,0.9578004265676212,0.9892633205852963,0.03928833421484908
 nvfp4,0.9927072006236084,0.921143514382719,0.9436932438894632,0.8901975713664323,0.9347693606993568,0.9708130087272056,0.8923405128580253,dinov2_large_image_cosine_to_bf16,0.7742967238004093,30,0.8702379679626541,0.9744959468668348,0.06233988811743947
+int4_convrot,0.9885934446494875,0.8797392477061481,0.9184911643398956,0.8291553317568755,0.9112576965374248,0.9477555261091675,0.853636623680778,dinov2_large_image_cosine_to_bf16,0.5978301303822195,30,0.8420643736753057,0.960088658862235,0.09997125868348651
+gguf_q8_0,0.9989298610671624,0.9799612789541914,0.9872860044561044,0.9699668381011598,0.9905676394491303,0.9954137575354709,0.971682603107787,dinov2_large_image_cosine_to_bf16,0.9051122966099583,30,0.9686377648486635,0.995658466685398,0.0220019097699819
+gguf_q4_k_m,0.9914807061388438,0.9340293255680715,0.9556034447646925,0.9027629041747575,0.9545968283410777,0.9730674727912509,0.9047583077214002,dinov2_large_image_cosine_to_bf16,0.7899225185134612,30,0.898328574520977,0.9735982534716395,0.05512314457582849
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,dists,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.15055304765701294,0.051304304599761964,0.06978587482632605,0.03982391999423102,0.042738109827041626,0.05922994017601013,0.03169947862625122,dists,0.017882049083709717,30,0.0299847275018692,0.06542082130908966,0.031550285048114246
+int8_convrot,0.11818915605545044,0.03466375072797139,0.049908950680957465,0.02503633177239456,0.026795655488967896,0.03763192892074585,0.0206071138381958,dists,0.003576338291168213,30,0.01743854582309723,0.04030798375606537,0.02801484933406217
+mxfp8,0.1401481032371521,0.04750467340151469,0.06711637076102364,0.03476623075476806,0.035131752490997314,0.0532895028591156,0.03017646875185167,dists,0.004149675369262695,30,0.029238015413284302,0.05851832032203674,0.033330181739859764
+nvfp4,0.19701147079467773,0.08801797827084859,0.10204016213523202,0.07653043704416197,0.08443576097488403,0.101826012134552,0.06442761421203613,dists,0.03400731086730957,30,0.061743080615997314,0.11066558957099915,0.03621241900517073
+int4_convrot,0.17702502012252808,0.12543688615163168,0.1369599512166738,0.11351881173892175,0.13264748454093933,0.13990554213523865,0.11125904321670532,dists,0.0731390118598938,30,0.11017036437988281,0.14528340101242065,0.027280720580522343
+gguf_q8_0,0.12237286567687988,0.033796403805414835,0.050517101030767855,0.02391100492970594,0.024558991193771362,0.04111379384994507,0.016328811645507812,dists,0.004444301128387451,30,0.014626190066337585,0.0488194078207016,0.03025125586084626
+gguf_q4_k_m,0.13136398792266846,0.07589179476102194,0.08900632359113562,0.06476540160679556,0.06629142165184021,0.09222069382667542,0.0543857216835022,dists,0.03494572639465332,30,0.0522821843624115,0.09956756234169006,0.027637467069630696
+bf16,0.3447265625,0.27633056640625,0.2973443217993806,0.25089740725404247,0.2877197265625,0.308349609375,0.24310302734375,hpsv2_1,0.171142578125,30,0.244293212890625,0.31591796875,0.04651941224206469
+fp8_scaled,0.353271484375,0.27591552734375,0.2971720377604167,0.25152587890625,0.2857666015625,0.29736328125,0.2432861328125,hpsv2_1,0.177978515625,30,0.2413330078125,0.303955078125,0.04584655460162336
+int8_convrot,0.34619140625,0.27720540364583335,0.2979248046875,0.25203198506776453,0.28857421875,0.3095703125,0.2440185546875,hpsv2_1,0.17333984375,30,0.24517822265625,0.3150634765625,0.04610534438911367
+mxfp8,0.35205078125,0.276611328125,0.29768473307291665,0.25119830452813957,0.2896728515625,0.3048095703125,0.23974609375,hpsv2_1,0.171875,30,0.239959716796875,0.3074951171875,0.04670293512810871
+nvfp4,0.348876953125,0.2723795572916667,0.2933544879934367,0.2468966758232989,0.28564453125,0.2987060546875,0.236083984375,hpsv2_1,0.16455078125,30,0.23712158203125,0.3023681640625,0.046515017925069436
+int4_convrot,0.360595703125,0.27129720052083334,0.2940300277330276,0.24406670723930615,0.2864990234375,0.2998046875,0.232421875,hpsv2_1,0.1678466796875,30,0.23358154296875,0.302734375,0.05026433040140601
+gguf_q8_0,0.35107421875,0.27657877604166664,0.2976394644736054,0.25149739583333336,0.2867431640625,0.305908203125,0.2431640625,hpsv2_1,0.173828125,30,0.241455078125,0.307861328125,0.04657632596151615
+gguf_q4_k_m,0.34814453125,0.272607421875,0.2934651692708333,0.24733514061711268,0.28515625,0.302734375,0.2430419921875,hpsv2_1,0.170654296875,30,0.243316650390625,0.307373046875,0.0466383672297874
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,lpips_alex,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.3820990324020386,0.1264743079120914,0.18625874261914185,0.08270866140658097,0.09370071068406105,0.17221540212631226,0.04312717542052269,lpips_alex,0.021495753899216652,30,0.042709083296358585,0.17643478512763977,0.10711371911167711
+int8_convrot,0.28394433856010437,0.07244204020438096,0.1073777973725979,0.04725852738371055,0.04186439700424671,0.08600236475467682,0.02455641608685255,lpips_alex,0.005779309663921595,30,0.023480938747525215,0.09854498319327831,0.07215048423581227
+mxfp8,0.3678184747695923,0.10497030741535127,0.15554451843046357,0.06797323619550298,0.07122896611690521,0.1423274278640747,0.03228048235177994,lpips_alex,0.0031711203046143055,30,0.03099331259727478,0.15937044098973274,0.09732756936551985
+nvfp4,0.44609522819519043,0.21921894767632086,0.2616719525324047,0.1718276085328811,0.20512405037879944,0.2915562391281128,0.14014990627765656,lpips_alex,0.024270767346024513,30,0.13827631622552872,0.305231548845768,0.11252626171472946
+int4_convrot,0.5811458230018616,0.3219427287578583,0.3903633518012353,0.25173804133219274,0.3147275298833847,0.42824432253837585,0.20592859387397766,lpips_alex,0.06495121121406555,30,0.19599925726652145,0.4350004643201828,0.14858332009640987
+gguf_q8_0,0.32637086510658264,0.07161818519234657,0.11622448757877843,0.04494170486172527,0.04261758178472519,0.08880101144313812,0.01815104391425848,lpips_alex,0.0032204180024564266,30,0.01340003334917128,0.11634491570293903,0.0778392920187458
+gguf_q4_k_m,0.37078195810317993,0.18404781532784303,0.23395858958744575,0.1364341146223375,0.1532520204782486,0.24530509114265442,0.09799712151288986,lpips_alex,0.030456528067588806,30,0.0967559888958931,0.26177120208740234,0.1074229703808229
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,lpips_vgg,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.3665781617164612,0.14539796921114126,0.20123727477280212,0.1024217312374525,0.09543363004922867,0.18791768699884415,0.05986759997904301,lpips_vgg,0.0344182550907135,30,0.05911336001008749,0.21097444370388985,0.10244581089377869
+int8_convrot,0.30687659978866577,0.08575619366019964,0.12138620848125187,0.060260746336359965,0.050026947632431984,0.1094667911529541,0.034907590597867966,lpips_vgg,0.012240605428814888,30,0.034275008365511894,0.1303403116762638,0.07150329391836473
+mxfp8,0.35648101568222046,0.12272146384542187,0.17056539458576614,0.08620750131698147,0.0947742909193039,0.1796317845582962,0.05934765189886093,lpips_vgg,0.014053110964596272,30,0.0589706227183342,0.19296270608901978,0.09079705969066877
+nvfp4,0.44362935423851013,0.24042122587561607,0.28090796375735066,0.1982602925538945,0.2236902341246605,0.31399184465408325,0.18850541859865189,lpips_vgg,0.07175146043300629,30,0.1702096089720726,0.32828274369239807,0.10108864041707974
+int4_convrot,0.6063505411148071,0.3506282995144526,0.4056376737176297,0.3012407737400253,0.33684250712394714,0.42269468307495117,0.27550190687179565,lpips_vgg,0.17532223463058472,30,0.2674302011728287,0.434578500688076,0.11182686236550719
+gguf_q8_0,0.32499998807907104,0.08496197331696749,0.12823456054078602,0.05722080347294085,0.0550285205245018,0.11510779708623886,0.03076383750885725,lpips_vgg,0.004857093095779419,30,0.02981826849281788,0.14556844532489777,0.07668850594065887
+gguf_q4_k_m,0.398654580116272,0.2073124445974827,0.2544996279250258,0.16442673388569282,0.18820242583751678,0.2634657025337219,0.12846118211746216,lpips_vgg,0.07466328889131546,30,0.12124519608914852,0.2814710959792137,0.1005294351145594
 bf16,1.0,1.0,1.0,1.0,1.0,1.0,1.0,ms_ssim,1.0,30,1.0,1.0,0.0
+fp8_scaled,0.9930009841918945,0.8483239730199178,0.9084990327492932,0.7547556166882755,0.869319498538971,0.9625673294067383,0.8052575290203094,ms_ssim,0.43679410219192505,30,0.7964089959859848,0.9683683663606644,0.15242192268740185
+int8_convrot,0.9970208406448364,0.9166152695814769,0.9496642273638194,0.8550445410173996,0.9561639130115509,0.9839557409286499,0.9065420627593994,ms_ssim,0.5410804748535156,30,0.8975338190793991,0.9885507076978683,0.10305741897618577
+mxfp8,0.9977141618728638,0.8777395576238632,0.9255420200710697,0.7920319987248211,0.9172250628471375,0.9839997291564941,0.8452204763889313,ms_ssim,0.44286975264549255,30,0.825881615281105,0.9876376092433929,0.13564754304482293
+nvfp4,0.9860314130783081,0.7496655414501826,0.8174127541458134,0.6774230441580816,0.7687365114688873,0.8618973195552826,0.6211484670639038,ms_ssim,0.42906951904296875,30,0.5945302695035934,0.8774771094322205,0.16538902192354002
+int4_convrot,0.9679651260375977,0.658824426929156,0.7519351554629595,0.5632870644912248,0.6427672505378723,0.7973213195800781,0.5159263908863068,ms_ssim,0.2940448522567749,30,0.5152721256017685,0.823727935552597,0.19825342269859877
+gguf_q8_0,0.9988769888877869,0.9161211550235748,0.9518260902014738,0.8422306383646682,0.9526853859424591,0.9911565780639648,0.874070554971695,ms_ssim,0.4878024458885193,30,0.866970106959343,0.9929526001214981,0.11036125460810206
+gguf_q4_k_m,0.9910087585449219,0.7919322818517684,0.8606452808274617,0.7145820075656139,0.798898845911026,0.9215598702430725,0.7213739156723022,ms_ssim,0.4318520724773407,30,0.6792362779378891,0.9262368679046631,0.15538579788412427
 bf16,7.0,0.9333333333333333,2.1333333333333333,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.9640446187314116
 fp8_scaled,6.0,0.8666666666666667,1.8666666666666667,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.7952683659406263
 int8_convrot,7.0,0.9333333333333333,2.1333333333333333,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.9640446187314116
 mxfp8,7.0,0.9,2.0,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.9000907419347655
+nvfp4,7.0,0.9333333333333333,2.1666666666666665,0.26666666666666666,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.9640446187314111
+int4_convrot,6.0,0.9333333333333333,2.1333333333333333,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.946408422486513
+gguf_q8_0,8.0,0.9,2.0,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.971346467912661
+gguf_q4_k_m,4.0,0.7666666666666667,1.6,0.0,0.0,0.0,0.0,ocr_recognized_line_count,0.0,30,0.0,0.0,1.568731825465999
+bf16,1.000000125266627,0.9999999940734259,1.0000000141910794,0.999999969473375,0.9999999923568554,1.0000000049637345,0.9999999617486633,openclip_vith14_image_cosine_to_bf16,0.9999998769699886,30,0.9999999471789609,1.0000000300142222,6.385695605784303e-08
+fp8_scaled,0.9965124998783443,0.9791456502532384,0.9852222198590554,0.9696768922130987,0.9863771911451533,0.989969561094228,0.974226961250514,openclip_vith14_image_cosine_to_bf16,0.9313569096429853,30,0.9731082294139901,0.9910437253915281,0.016882672154409112
+int8_convrot,0.9984301021934913,0.988199698017437,0.992057110822077,0.9814200573576358,0.993389964999944,0.9951276246734035,0.9839241501162583,openclip_vith14_image_cosine_to_bf16,0.940550418707031,30,0.9818777282732937,0.9964521522736833,0.012568302358385589
+mxfp8,0.9992730458008162,0.9819962665894467,0.9874015691250904,0.973198318277606,0.9870062383489837,0.9896330012876107,0.9819852106407714,openclip_vith14_image_cosine_to_bf16,0.9318314106031923,30,0.9805235806302957,0.9913865440104338,0.01599301731225812
+nvfp4,0.9912252998774632,0.958928857737406,0.9665945685314776,0.9492745509886698,0.9602555988555297,0.9666151067279733,0.9525271345988313,openclip_vith14_image_cosine_to_bf16,0.890382264515351,30,0.9495445747135758,0.9763968999286123,0.023856647902892405
+int4_convrot,0.9876509999570782,0.9358737633573803,0.9497959565363328,0.9181532836742164,0.9462415885223231,0.9578240354699025,0.9164440142592085,openclip_vith14_image_cosine_to_bf16,0.8387275159607446,30,0.9099625896485063,0.960782174586309,0.034411418249800645
+gguf_q8_0,0.9987256809463121,0.9880865902818792,0.9915714453145625,0.9823098170013332,0.9908825270197752,0.9964932456318629,0.983805452001485,openclip_vith14_image_cosine_to_bf16,0.9615455220076895,30,0.9832659679085268,0.9966838814284764,0.010436481549087759
+gguf_q4_k_m,0.9916788527011178,0.9669160508560661,0.973849014342115,0.9569490916206972,0.9723147188803174,0.979873649463301,0.9572883294528889,openclip_vith14_image_cosine_to_bf16,0.9246293738786993,30,0.9546469852078003,0.9809999742574852,0.018762559601764484
+bf16,51.275699615478516,41.73448168436686,44.36252439097732,39.19454726451913,41.32732963562012,44.64219665527344,37.6806755065918,openclip_vith14_prompt_score,32.6300048828125,30,37.64154624938965,46.55621528625488,5.296917473155288
+fp8_scaled,51.483497619628906,41.69884287516276,44.21863258300701,39.38238897961775,41.257368087768555,45.165687561035156,37.25627899169922,openclip_vith14_prompt_score,34.211238861083984,30,37.541443824768066,45.91065788269043,4.960724074952656
+int8_convrot,51.184898376464844,41.74314206441243,44.36214682642037,39.22715620328133,41.76202392578125,44.18941116333008,37.436418533325195,openclip_vith14_prompt_score,32.58320236206055,30,37.313395500183105,46.82499980926514,5.236417859366287
+mxfp8,51.27956008911133,41.81309967041015,44.4804831635057,39.227317263288036,41.58754348754883,44.63146209716797,37.887760162353516,openclip_vith14_prompt_score,32.4578857421875,30,37.85694694519043,46.197611808776855,5.356400715433708
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@@ -4,163 +4,262 @@ fp8_scaled,0.22619915008544922,0.01584672927856445,0.04660426589656906,0.0035170
 int8_convrot,0.2126582463582357,0.015050061543782555,0.047096149377796305,0.0033317442824674446,0.0014796257019042969,0.004377841949462891,1.5417734781901043e-05,artifact_channel_clipping_fraction,0.0,30,1.2318293253580729e-05,0.0057706038157145185,0.04100993101200461
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 nvfp4,0.14287535349527994,0.010600037044949,0.03136560691746088,0.002320763345189201,0.0006882349650065105,0.0029071172078450522,6.421407063802083e-05,artifact_channel_clipping_fraction,0.0,30,7.001558939615885e-05,0.0039368470509847,0.027980738723959432
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-mxfp8,0.001796722412109375,7.448196411132812e-05,0.00035491143279784933,3.337860107421875e-06,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,2.86102294921875e-06,0.0003299410262263989
-nvfp4,0.0004730224609375,2.5177001953125e-05,8.939107259114583e-05,5.753835042317708e-06,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,1.0967254638671875e-05,8.7420620015132e-05
-bf16,0.0026324703358113766,0.0012742011866066604,0.001540991125819626,0.0011054383270773893,0.0011953407083638012,0.0013988425489515066,0.0010628745658323169,artifact_flat_region_chroma_noise,0.0003196971374563873,30,0.0010434366413392127,0.0014493349590338767,0.0004371060700129983
-fp8_scaled,0.002659283112734556,0.0012697198738654454,0.0015311802903712226,0.00109598487314873,0.001191404415294528,0.0013635556679219007,0.0010579403024166822,artifact_flat_region_chroma_noise,0.00026580505073070526,30,0.0010307297634426504,0.001444745052140206,0.00044712446368358726
-int8_convrot,0.0026403230149298906,0.00127472765937758,0.0015393966977268394,0.0011050619010504032,0.0011949676554650068,0.001417321851477027,0.0010504998499527574,artifact_flat_region_chroma_noise,0.0003394151572138071,30,0.0010300807771272957,0.0014406022382900119,0.00043755561264026866
-mxfp8,0.0026044989936053753,0.0012747316640646508,0.0015339034980984363,0.0011173933418710132,0.001196873839944601,0.0013951604487374425,0.0010489965206943452,artifact_flat_region_chroma_noise,0.0005627128994092345,30,0.0010335321421734989,0.0014184775936882943,0.0004112971785737646
-nvfp4,0.002881864085793495,0.0013037522012988726,0.0016408585263600727,0.00113765598125503,0.0011877098586410284,0.0014002487296238542,0.0010488347616046667,artifact_flat_region_chroma_noise,0.0006382025312632322,30,0.0010546765406616032,0.0014527786406688392,0.0004666635390877322
-bf16,6.416837584264616,5.630332298254742,5.937762536985776,5.128547564766798,5.934040832522571,6.146767374611394,5.258676736978931,artifact_flat_region_gradient_entropy_bits,2.081308045850826,30,5.2682502854271425,6.192119795427443,0.9011274075403761
-fp8_scaled,6.333523352792442,5.601924281404789,5.932859142805558,5.076990967411112,6.004031895442508,6.147973108204308,5.3368821121190475,artifact_flat_region_gradient_entropy_bits,1.78379743857564,30,5.319295131006731,6.15346466603812,0.960667470081641
-int8_convrot,6.389288099922436,5.634416491318564,5.933510620295589,5.14313809488075,5.938448086535914,6.120962602204186,5.304770523106146,artifact_flat_region_gradient_entropy_bits,2.179185380146164,30,5.2832052790499935,6.195056174030415,0.8868561651273056
-mxfp8,6.336303336115902,5.6826921400618495,5.9573716891179815,5.2864994358435125,5.953401821241342,6.132035892335772,5.269578304671067,artifact_flat_region_gradient_entropy_bits,3.3418166934141413,30,5.248905689176285,6.168361318297629,0.7313146335757138
-nvfp4,6.286260143740057,5.680537621693147,5.9643849319826945,5.2409183499792364,6.008914027761253,6.172456878839885,5.4325163153973755,artifact_flat_region_gradient_entropy_bits,3.67290958965281,30,5.439807557277379,6.178694902309257,0.744424752329161
-bf16,0.7955513000488281,0.13440872206124965,0.2366936461307333,0.07025544303317474,0.05450516607505913,0.17257664881716345,0.017265583276112002,artifact_flat_region_near_zero_gradient_fraction,0.006924018906577246,30,0.018747927625392624,0.19160499226559563,0.18103009860272135
-fp8_scaled,0.8287536669756654,0.13920717341054653,0.24453851496256804,0.07244688510720713,0.05563413500106798,0.17328643798828125,0.0213623046875,artifact_flat_region_near_zero_gradient_fraction,0.005779266357421875,30,0.02225401394762204,0.19564828211489171,0.18841612933729052
-int8_convrot,0.77838433778144,0.1329126708709066,0.23374393530183582,0.06905376111046907,0.05279434487081617,0.17612883221580558,0.014584756011490788,artifact_flat_region_near_zero_gradient_fraction,0.0068439585089784035,30,0.016851347770336842,0.1959620507691832,0.17905842696004376
-mxfp8,0.6211738586425781,0.12626263689575995,0.21402214923044888,0.06781565370580958,0.05425506154264905,0.19073940528431582,0.01610111530981465,artifact_flat_region_near_zero_gradient_fraction,0.007702050065232816,30,0.01814025146038833,0.1959309682312283,0.15595269837596937
-nvfp4,0.5716285705566406,0.12654183438988204,0.2201001193470928,0.06612328743500168,0.057048203474104664,0.1744071241149601,0.019208879857604486,artifact_flat_region_near_zero_gradient_fraction,0.003456313494500799,30,0.019502141765761958,0.17690198326911827,0.1581650255524848
-bf16,0.056511349976062775,0.021361673747499785,0.029767162798345,0.015741249546754155,0.0176676157861948,0.024389423429965973,0.010799922980368137,artifact_gradient_mean,0.003567072097212076,30,0.011350627057254314,0.027287392411381006,0.013702531639900848
-fp8_scaled,0.05567804351449013,0.021355037088505925,0.029239240685604986,0.015825707591768136,0.017760617658495903,0.024680212140083313,0.010581627488136292,artifact_gradient_mean,0.0034956838935613632,30,0.011143338168039918,0.0270552565343678,0.013279397653896324
-int8_convrot,0.0569881908595562,0.02146767466329038,0.029921673002517524,0.015912405342223223,0.017675630748271942,0.024423032999038696,0.010942107997834682,artifact_gradient_mean,0.0035396190360188484,30,0.011450093472376466,0.02682424569502473,0.01362559006919798
-mxfp8,0.05627179518342018,0.0213521351882567,0.029344328857101427,0.01588954623408864,0.017737149260938168,0.024058064445853233,0.010880529880523682,artifact_gradient_mean,0.003583754412829876,30,0.011368897743523121,0.026537164580076933,0.013245222570898496
-nvfp4,0.0586092434823513,0.021582980919629336,0.030289764443063735,0.015935818525672423,0.01730195339769125,0.02393435873091221,0.011776251718401909,artifact_gradient_mean,0.003514604177325964,30,0.011827467940747738,0.026330961380153894,0.013842015937051007
-bf16,0.3292927145957947,0.09418034599317858,0.15056780533357336,0.059237714662662734,0.06720578297972679,0.13138848543167114,0.03358621150255203,artifact_gradient_p95,0.003680284135043621,30,0.03444353397935629,0.13169771060347557,0.08766469603546928
-fp8_scaled,0.3210466206073761,0.09442543711823721,0.1504171073796864,0.059820649718999606,0.06644310429692268,0.1300525963306427,0.03172677755355835,artifact_gradient_p95,0.0041948952712118626,30,0.03323384188115597,0.13058029860258102,0.0877321228392554
-int8_convrot,0.3253588080406189,0.09409170714206994,0.15127621373863548,0.0594105944553415,0.06683020293712616,0.1302701085805893,0.03470975160598755,artifact_gradient_p95,0.004462540149688721,30,0.03535366430878639,0.1310233175754547,0.08811219837040019
-mxfp8,0.3153921365737915,0.09463084110369285,0.1507388021667002,0.06030267450454657,0.06711289286613464,0.1291860193014145,0.03329947218298912,artifact_gradient_p95,0.0084872180595994,30,0.034185162745416164,0.13009042665362358,0.08690864943230711
-nvfp4,0.3138689696788788,0.09321397002786398,0.14773624965340626,0.05966034583226396,0.06597977876663208,0.12111478298902512,0.03338415175676346,artifact_gradient_p95,0.0053109717555344105,30,0.03388739004731178,0.12799899280071259,0.0854955066599613
-bf16,1.0906498432159424,0.9770338594913482,1.0053575953623046,0.923303643079813,0.9982165992259979,1.0083772540092468,0.9856676459312439,artifact_grid_boundary_ratio_16,0.6771283745765686,30,0.9850530177354813,1.0107260942459106,0.08442361455313704
-fp8_scaled,1.0720762014389038,0.9715812047322591,0.9974974851442394,0.8912271241402175,0.9923600256443024,1.007717788219452,0.9810031652450562,artifact_grid_boundary_ratio_16,0.6666409969329834,30,0.9800843447446823,1.009460210800171,0.08907237426539048
-int8_convrot,1.0902608633041382,0.9876294732093811,1.0092216454110565,0.9532488826577142,1.0009598433971405,1.0091087818145752,0.9866995215415955,artifact_grid_boundary_ratio_16,0.6882171034812927,30,0.9814575612545013,1.009639948606491,0.07676395410790642
-mxfp8,1.044888973236084,0.9596982359886169,0.9954417168606412,0.8748330023727818,1.0028482675552368,1.0104074478149414,0.9799090623855591,artifact_grid_boundary_ratio_16,0.5884047150611877,30,0.9791912138462067,1.0121881365776062,0.1080430570106408
-nvfp4,1.0808591842651367,0.9804623285929362,0.9980791693298092,0.9425520292723797,0.9973191618919373,1.0119941234588623,0.9838801026344299,artifact_grid_boundary_ratio_16,0.693781852722168,30,0.9811625927686691,1.0137655436992645,0.07289492791312625
-bf16,1.1975072622299194,0.9849390625953675,1.0256157932639232,0.8997706996192992,1.0019777417182922,1.0187100172042847,0.9867652356624603,artifact_grid_boundary_ratio_32,0.6262194514274597,30,0.9794457405805588,1.0198944211006165,0.1285929966104787
-fp8_scaled,1.2139098644256592,0.9762315491835276,1.0121229657250916,0.8966597402115278,0.9852725565433502,1.0069315433502197,0.967332124710083,artifact_grid_boundary_ratio_32,0.6390887498855591,30,0.9645107239484787,1.0136566758155823,0.11230165059830863
-int8_convrot,1.1980863809585571,0.9879613757133484,1.0207911848910367,0.902193464295719,0.9921569526195526,1.0086103081703186,0.9791041016578674,artifact_grid_boundary_ratio_32,0.5951148271560669,30,0.9655886143445969,1.0215989649295807,0.11665248284835955
-mxfp8,1.1549221277236938,0.9599421600500743,1.0029825614923682,0.8521390951264124,0.9903554618358612,1.0098696947097778,0.9678680896759033,artifact_grid_boundary_ratio_32,0.4989204406738281,30,0.9596565067768097,1.0138724148273468,0.13526229564695805
-nvfp4,1.3504304885864258,1.0145842552185058,1.0366422986954769,0.9978759588566091,0.9950121343135834,1.007063627243042,0.9869824051856995,artifact_grid_boundary_ratio_32,0.7020969390869141,30,0.9838045388460159,1.0131991505622864,0.10743534351928993
-bf16,1.7820409536361694,1.0122021993001302,1.0900631450376321,0.9820396526367035,0.9941430985927582,1.0006558895111084,0.9841523766517639,artifact_grid_boundary_ratio_8,0.8372174501419067,30,0.9797354936599731,1.0009718537330627,0.15303746960452702
-fp8_scaled,1.0370781421661377,0.9766763130823771,0.997619566613378,0.9340444653243849,0.9937796294689178,0.99673792719841,0.9822983145713806,artifact_grid_boundary_ratio_8,0.6835401654243469,30,0.9782261848449707,1.0023436844348907,0.06956089564901081
-int8_convrot,1.3303442001342773,1.0031312664349874,1.0565388751175648,0.980716595793952,0.99241703748703,1.0007754266262054,0.9844188392162323,artifact_grid_boundary_ratio_8,0.8404008150100708,30,0.9766575247049332,1.0063539445400238,0.0770690924429621
-mxfp8,1.2602901458740234,0.9853387792905172,0.9993631811340897,0.94828931200808,0.9932082295417786,1.0011764466762543,0.9804414510726929,artifact_grid_boundary_ratio_8,0.6638944745063782,30,0.9795538187026978,1.0084186494350433,0.09293145789930513
-nvfp4,1.3248720169067383,0.9853103339672089,0.9980940351863301,0.9564813754144006,0.9893903136253357,0.9983897507190704,0.979004293680191,artifact_grid_boundary_ratio_8,0.7003074288368225,30,0.9786079376935959,1.006067544221878,0.09081610119986813
-bf16,0.031559523195028305,0.0064735145075246695,0.012777174001295296,0.004089403519722057,0.005311400163918734,0.006391802337020636,0.0018934495165012777,artifact_high_frequency_power_ratio,0.001192638766951859,30,0.0018877777911256999,0.006658871774561703,0.007328949212130287
-fp8_scaled,0.031355783343315125,0.006476964879160126,0.011834933676320134,0.004156078316862965,0.005249088630080223,0.006370231043547392,0.0018408752512186766,artifact_high_frequency_power_ratio,0.0011659483425319195,30,0.001846569066401571,0.007510064169764519,0.006901406391241682
-int8_convrot,0.02969740703701973,0.0065337488466563325,0.011999091315211027,0.004203088322560123,0.005146111128851771,0.006715714931488037,0.0018497201381251216,artifact_high_frequency_power_ratio,0.0011473443591967225,30,0.0018189831753261387,0.0077858156291767955,0.006933389429983453
-mxfp8,0.03157830238342285,0.006602075016902139,0.012122125633585506,0.004239740459635961,0.005066586658358574,0.006683299783617258,0.0018839709809981287,artifact_high_frequency_power_ratio,0.0012652649311348796,30,0.001857430295785889,0.007654394372366369,0.007022572197051972
-nvfp4,0.0306825190782547,0.006704863568302244,0.012177573152926495,0.004293608452802023,0.005577087635174394,0.006991913542151451,0.00184023124165833,artifact_high_frequency_power_ratio,0.001274063135497272,30,0.0018215342715848237,0.00757640996016562,0.007038282253146066
-bf16,0.027782432734966278,0.0053362832996450985,0.011061095828641569,0.003137920091856851,0.003395178122445941,0.005987070966511965,0.001374211278744042,artifact_laplacian_variance,8.004248957149684e-05,30,0.001399801200022921,0.006332668359391391,0.006654903275292051
-fp8_scaled,0.02867905981838703,0.005425079094129615,0.010834098647944945,0.0032263653104674917,0.003414097474887967,0.005817532539367676,0.0014249489759095013,artifact_laplacian_variance,7.772165554342791e-05,30,0.0014136020035948604,0.006825885968282819,0.0066070968702255034
-int8_convrot,0.028111876919865608,0.00541458202302844,0.011223221245360324,0.003180133436354879,0.0033844446297734976,0.005581424804404378,0.0014178546844050288,artifact_laplacian_variance,7.867597742006183e-05,30,0.0014246410864870995,0.006812799721956253,0.00675362272172227
-mxfp8,0.028923794627189636,0.005612394586448014,0.011150997356894047,0.003253984048420919,0.0034550182754173875,0.0056879278272390366,0.001412491430528462,artifact_laplacian_variance,8.621600136393681e-05,30,0.001438404928194359,0.006638265214860439,0.006930931305939735
-nvfp4,0.027543842792510986,0.005553710185874176,0.011099870453994128,0.00320995379552079,0.0036439052782952785,0.006389327347278595,0.0011907730950042605,artifact_laplacian_variance,7.635269139427692e-05,30,0.0012419656559359282,0.00649238598998636,0.006828000006716776
-bf16,0.8287761211395264,0.47474102502067883,0.5840795680677426,0.3651207439009339,0.4818723201751709,0.5673525035381317,0.2734641134738922,artifact_luminance_mean,0.12227670103311539,30,0.29137982428073883,0.6666537672281265,0.22245731239671693
-fp8_scaled,0.8275262117385864,0.4760946253935496,0.5860480070410832,0.36616458356876097,0.4847806692123413,0.5677300095558167,0.26170313358306885,artifact_luminance_mean,0.12329041957855225,30,0.29098669439554214,0.6661370992660522,0.2221189775115306
-int8_convrot,0.8290361166000366,0.47413557867209116,0.5867673430135,0.36673726335853546,0.48251913487911224,0.5583099126815796,0.2752035856246948,artifact_luminance_mean,0.1225673109292984,30,0.2927829548716545,0.6650997698307037,0.22119951841336072
-mxfp8,0.8272250890731812,0.47249420881271365,0.5823757175290472,0.3648921214782582,0.48022058606147766,0.5639391541481018,0.2797144651412964,artifact_luminance_mean,0.12252219021320343,30,0.2956392541527748,0.6592095792293549,0.22000155515524206
-nvfp4,0.8154351115226746,0.4781216373046239,0.5873956422201388,0.36889446494660416,0.4895312786102295,0.5763062238693237,0.2722606062889099,artifact_luminance_mean,0.12202876806259155,30,0.29260285198688507,0.6778658777475357,0.22109535376215295
-bf16,0.29011160135269165,0.21992382928729057,0.24333641495070765,0.18429574055907966,0.24474745988845825,0.25892728567123413,0.1800270974636078,artifact_luminance_std,0.07326649874448776,30,0.1816285066306591,0.2615939676761627,0.05855414473314542
-fp8_scaled,0.293201208114624,0.220692940056324,0.24354457576876076,0.184052651870349,0.24421841651201248,0.25936856865882874,0.18172059953212738,artifact_luminance_std,0.07360288500785828,30,0.18372415378689766,0.26235271245241165,0.058123737993796436
-int8_convrot,0.2897592782974243,0.22030811284979185,0.2440273928422556,0.18512907283887187,0.24509183317422867,0.2589303106069565,0.1781918704509735,artifact_luminance_std,0.07315973937511444,30,0.18125183880329132,0.26139361411333084,0.0586937316382405
-mxfp8,0.29982978105545044,0.22149013926585515,0.24558420065834055,0.18516854036769193,0.2470528706908226,0.2577151358127594,0.17928209900856018,artifact_luminance_std,0.0733739584684372,30,0.18124116584658623,0.26193416863679886,0.05967059698460225
-nvfp4,0.28884589672088623,0.21807041441400846,0.2398629277026269,0.1822739268937721,0.23719629645347595,0.2506242170929909,0.18501295149326324,artifact_luminance_std,0.07255903631448746,30,0.1872648522257805,0.25252117961645126,0.055867771197170416
-bf16,0.34344542026519775,0.11398565527051688,0.16781476992359146,0.07785031976368292,0.10549644008278847,0.14687331020832062,0.04765601083636284,artifact_saturation_mean,0.008430104702711105,30,0.04829507227987051,0.16222890466451645,0.08755793359108803
-fp8_scaled,0.34116125106811523,0.11448063325757782,0.16831656101868514,0.07734908511497568,0.10298968479037285,0.14753016829490662,0.04662243276834488,artifact_saturation_mean,0.00894435029476881,30,0.047263165935873985,0.1612839177250862,0.08896540188546719
-int8_convrot,0.34348899126052856,0.11391610754653811,0.16693367408016296,0.07765961859946505,0.10668217018246651,0.14877855777740479,0.04415845613143083,artifact_saturation_mean,0.008413145318627357,30,0.04681415855884552,0.16164465248584747,0.08733128433565733
-mxfp8,0.34628283977508545,0.11385334435229501,0.16799360971765728,0.07733508792254629,0.10478852689266205,0.1456046849489212,0.04750455170869827,artifact_saturation_mean,0.009984525851905346,30,0.04804812744259834,0.1575765237212181,0.08859439675565352
-nvfp4,0.33353155851364136,0.11345184243594607,0.16597897469014436,0.07707688929054628,0.11028828471899033,0.13931508362293243,0.04518362879753113,artifact_saturation_mean,0.0062842704355716705,30,0.047630670480430126,0.15444738790392876,0.08686385198765167
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,delta_e_2000_mean,0.0,30,0.0,0.0,0.0
-fp8_scaled,11.483121871948242,3.966377484798431,5.856709431800798,2.573107693846278,2.5886929035186768,4.748284816741943,1.3150166273117065,delta_e_2000_mean,0.42607879638671875,30,1.2700645327568054,4.9170098304748535,3.3691278175130255
-int8_convrot,9.37054443359375,2.3873159204920134,3.6996567760823904,1.6073717565379482,1.234503149986267,2.6648285388946533,0.6395715475082397,delta_e_2000_mean,0.23851720988750458,30,0.6368480324745178,3.561531722545624,2.2612009545558043
-mxfp8,10.63775634765625,3.3460651487112045,4.983550271177828,2.2415738971058152,2.6430084705352783,3.6377205848693848,0.9271849393844604,delta_e_2000_mean,0.44776690006256104,30,0.9909160435199738,4.94148850440979,2.8674949537710908
-nvfp4,19.292980194091797,6.659375993410746,8.497948219272647,5.062107102235623,6.313443660736084,8.464838981628418,3.4050838947296143,delta_e_2000_mean,0.7552700042724609,30,3.1281118392944336,9.676307439804077,4.2145226468747214
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,delta_e_2000_p95,0.0,30,0.0,0.0,0.0
-fp8_scaled,60.031036376953125,17.211933064460755,25.960691315765967,10.676399395412865,11.545272827148438,21.604257583618164,4.131968021392822,delta_e_2000_p95,1.0401486158370972,30,4.10766863822937,25.547958374023438,16.98805428704481
-int8_convrot,60.92300796508789,11.434780442714692,18.374381531620763,6.686750842014257,4.601085424423218,11.373390674591064,1.6462477445602417,delta_e_2000_p95,0.5516780614852905,30,1.6362426578998566,14.727276802062988,14.514765819904927
-mxfp8,60.938411712646484,15.235675468047459,22.98558989468761,9.584045879977454,11.239513874053955,18.10616683959961,3.1860376596450806,delta_e_2000_p95,0.7117034792900085,30,3.1227405667304993,20.36545753479004,15.725032370916187
-nvfp4,61.69163513183594,27.125506460666656,34.69876748145165,19.907316435655293,28.628643035888672,33.81364822387695,14.66044569015503,delta_e_2000_p95,1.7622984647750854,30,12.146420001983643,36.15382385253906,17.380972841306836
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,edge_mae,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.33001643419265747,0.07532161632552743,0.13948534220741898,0.04541790222636565,0.04912411421537399,0.0703529343008995,0.022591352462768555,edge_mae,0.0046882363967597485,30,0.021705199498683214,0.08493299782276154,0.08448712712844918
-int8_convrot,0.3080732226371765,0.05368041183489065,0.1099627573944841,0.03055915201673918,0.026883888989686966,0.04516151547431946,0.013133929576724768,edge_mae,0.0034100841730833054,30,0.012441205559298396,0.05733771622180939,0.07038598169635805
-mxfp8,0.30713704228401184,0.06807803912088276,0.12814154560007207,0.03978545128097407,0.04082764685153961,0.0697740912437439,0.018377656117081642,edge_mae,0.004183486104011536,30,0.018760444596409798,0.0701916553080082,0.08038695349965973
-nvfp4,0.35786503553390503,0.09647003185624878,0.15519462323432326,0.06365958410730842,0.0685483030974865,0.10237939655780792,0.03994151949882507,edge_mae,0.005974304862320423,30,0.03622992895543575,0.11117053590714931,0.0889229575086993
 bf16,1.0000000000000002,1.0,0.9999999999999999,0.9999999999999999,1.0,0.9999999999999998,0.9999999999999998,final_latent_cosine,0.9999999999999998,30,1.0,1.0000000000000002,1.6748780794631286e-16
-fp8_scaled,0.9990132257394989,0.95768181308319,0.9754938444974036,0.9320491723146647,0.974916844990766,0.9918454746848921,0.9381547628183944,final_latent_cosine,0.8440704265372225,30,0.9322940696767154,0.9947484577846772,0.0457573571854162
-int8_convrot,0.9996738629611984,0.9794700450363916,0.9880368421457525,0.9689157044818323,0.9911894084839659,0.996725111071858,0.9718072555648413,final_latent_cosine,0.9143871625882631,30,0.9675368644940532,0.9981169210243355,0.024724095004720455
-mxfp8,0.9997066815818643,0.9669332757282731,0.9808684451016044,0.9492246953656983,0.9796811619102106,0.9974308978066833,0.9505721327376957,final_latent_cosine,0.8904270511857791,30,0.9432470155029122,0.9977857851759111,0.03638925130596308
-nvfp4,0.997225161488146,0.9169226004348068,0.943314675389976,0.8871474884406081,0.9268460079540761,0.9646389444149731,0.8839675719848965,final_latent_cosine,0.6775479449230533,30,0.8705420260770691,0.9741240532265472,0.07239929399554912
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_mae,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.43088059438190385,0.16253725938964295,0.22745124326711993,0.11233126282399514,0.14401144072247973,0.21486968391621275,0.0632440157585279,final_latent_mae,0.0344473923613009,30,0.0590217649642355,0.22904379459394875,0.11869481578535535
-int8_convrot,0.34459674727068546,0.09785262226524007,0.1399007501548608,0.06733317787977415,0.061628942384125196,0.12062073918534111,0.0372752012475317,final_latent_mae,0.01399631493463005,30,0.03373658026252713,0.1379440010756241,0.084615045960451
-mxfp8,0.42524115449811006,0.1403025753338118,0.19707714067969764,0.09724860045196025,0.11666390966887263,0.1718260712149089,0.046522255345173846,final_latent_mae,0.019534103462888197,30,0.04567510139770903,0.20301869442146625,0.10845983560431778
-nvfp4,0.5718134701574229,0.26427991591832456,0.3134041794503163,0.20843075823296142,0.26637238939211727,0.35794007712409837,0.1643270599488389,final_latent_mae,0.06821991900285695,30,0.14772310937533106,0.3591660619070538,0.12819963476138754
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_max_abs,0.0,30,0.0,0.0,0.0
-fp8_scaled,6.810203313827515,3.4788162966569263,4.087974848751082,2.7948288981938774,3.94640189409256,4.331535577774048,2.2962564826011658,final_latent_max_abs,1.1445398926734924,30,2.2158609330654144,4.389311924576759,1.4032367657749338
-int8_convrot,6.822402715682983,3.0971722836295763,3.676014043484674,2.465755178608323,3.4214413315057755,3.803535223007202,2.2435614466667175,final_latent_max_abs,0.8671616911888123,30,2.1549638360738754,3.8457661867141724,1.4207052177754584
-mxfp8,6.87958025932312,3.235787672797839,3.81655613032647,2.5538031681567843,3.606418550014496,4.035384714603424,2.1296051666140556,final_latent_max_abs,1.0756072998046875,30,2.1120776124298573,4.108494818210602,1.3975605219273086
-nvfp4,7.554713249206543,4.248068203032017,4.914689402730678,3.5621107941078,4.420087456703186,4.746770858764648,3.782839208841324,final_latent_max_abs,1.5216014385223389,30,3.7823924124240875,4.86954802274704,1.4433200460136069
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 fp8_scaled,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 int8_convrot,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 mxfp8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 nvfp4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_relative_l2,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.561867670817564,0.24611432114340853,0.327688994395533,0.17683148301977236,0.22361785798622946,0.3219189353695148,0.10475190438122399,final_latent_relative_l2,0.04513061589334775,30,0.102422355026235,0.3656132232745428,0.15942733762085928
-int8_convrot,0.4117376784326574,0.16570607353080016,0.21631123117125764,0.11745399267955371,0.13257123231998846,0.22416861101912317,0.0627901055265302,final_latent_relative_l2,0.025538385579491816,30,0.06153399646256064,0.25466554422273574,0.11833088525091065
-mxfp8,0.4726395015778749,0.2139005903192808,0.28116533828016765,0.1489558637585221,0.2018998584626961,0.26427019504829696,0.06504099210841373,final_latent_relative_l2,0.024725070144718423,30,0.06670065480556368,0.3338293852941963,0.14614951286698435
-nvfp4,0.7917400959936337,0.3659206445697244,0.43586571645727307,0.28452914500533305,0.3813605818588721,0.4402367225121656,0.2523028629526829,final_latent_relative_l2,0.07451581635116171,30,0.2262810394511847,0.504546459842452,0.17796772156389645
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_rmse,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.7377951901563011,0.2997290529280748,0.4031188249421937,0.2115958866719488,0.27677320547453743,0.3869242548141378,0.12043358664606178,final_latent_rmse,0.05394278418661119,30,0.11010036654942262,0.4128623872869279,0.20276907999996566
-int8_convrot,0.7580860692740139,0.2053203749685123,0.28540012514247537,0.14471333340426232,0.1698773768622126,0.24011047247945894,0.07045042082196983,final_latent_rmse,0.029828283805020377,30,0.06929466972765312,0.2893305781935036,0.1687018116212049
-mxfp8,0.7843178276372679,0.2597711335533564,0.35170472710868017,0.1803018843113044,0.22786624558258123,0.29046383484470384,0.0874775293548552,final_latent_rmse,0.0359873609126252,30,0.08383933366756499,0.3597845159935686,0.193874026271923
-nvfp4,0.9401643557820462,0.43986440201573745,0.5325651468660103,0.34668356644355497,0.4359448089334799,0.5840122423774203,0.2859338339372067,final_latent_rmse,0.11212599804958107,30,0.24795566698553517,0.5929091233624896,0.21494028630006545
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,luminance_histogram_js,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.33825114263452427,0.01945370763599984,0.06400925056037511,0.006506634130788246,0.00332612580094384,0.009885450498588884,0.0018722693641762779,luminance_histogram_js,0.0003761274468946634,30,0.0017676893450690196,0.012086350192003482,0.061153551829226126
-int8_convrot,0.2956382769436881,0.015576551361549248,0.061623741183475106,0.003143353117505904,0.001579743705753635,0.0035642993122832678,0.0006561202953513216,luminance_histogram_js,0.00016029276910201263,30,0.0005123360598632729,0.004623427479476562,0.054033804935340435
-mxfp8,0.44879910510342297,0.03632349678009688,0.1325829790626381,0.009118514571529453,0.0023263563025843147,0.00572349126291466,0.001028710297362082,luminance_histogram_js,0.000311245546924982,30,0.000990672817108278,0.00839515983478614,0.09573689993782084
-nvfp4,0.2975578595152799,0.04419635920423148,0.08255751018498142,0.021047656004128803,0.012406239554403987,0.03667384142878227,0.006629875881504943,luminance_histogram_js,0.0026423989324661206,30,0.006316367910987301,0.04494439882821958,0.07304364824761002
 bf16,1.0000000000000002,1.0,1.0,1.0,1.0,0.9999999999999999,0.9999999999999999,pixel_cosine,0.9999999999999998,30,0.9999999999999999,1.0000000000000002,1.4723000231259014e-16
-fp8_scaled,0.9997174101836451,0.977177909784758,0.9878676104741805,0.9639499295373568,0.9887227846325779,0.9982173979477897,0.963447449301589,pixel_cosine,0.920929753854715,30,0.9613772315572804,0.9986170235867041,0.02546210798368741
-int8_convrot,0.9999191637627433,0.9886657918554602,0.9935474745929728,0.9812158966852553,0.9953487300694817,0.9987785009019261,0.9851095801009797,pixel_cosine,0.9483690245205272,30,0.9806195740889176,0.9992983581745922,0.013768144113868737
-mxfp8,0.999908597658179,0.983809107885415,0.9906735993871265,0.974998759575487,0.9867104253517991,0.9989682934002789,0.9737984215809845,pixel_cosine,0.9462661755937913,30,0.9732533670824269,0.9990265944457568,0.016655771843030468
-nvfp4,0.9993925868579964,0.9571523024286236,0.9734589024084855,0.9361018960062378,0.9703988935282456,0.9896606417752621,0.9321832728578532,pixel_cosine,0.8589949462434774,30,0.9313707413549633,0.9907761836811553,0.04152474355923369
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,pixel_mae,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.12151041816029344,0.039640880672782756,0.058860452518336104,0.025990787527904683,0.030032288642511652,0.048271662036881935,0.014071846091686286,pixel_mae,0.0035665421485949387,30,0.012675566307128369,0.04856900256260133,0.033664231274267785
-int8_convrot,0.10591174032151685,0.02391713974267936,0.038764294151163656,0.01601408045659759,0.013695026543246058,0.02816997383384508,0.0065201610242032375,pixel_mae,0.0018026253486217076,30,0.006405253760211356,0.034092428643456664,0.02377175939197411
-mxfp8,0.12146794873660838,0.033667628081583544,0.051708371899821144,0.02260088723764945,0.02887947293072557,0.03703606275701077,0.00874234130272574,pixel_mae,0.003720539321875549,30,0.009623546750996564,0.045891529829380794,0.0296620873935028
-nvfp4,0.21235614602725641,0.06762622004486428,0.08656531563529771,0.05095502925667417,0.061716821261446356,0.08697530288956538,0.0345933651303009,pixel_mae,0.006045911217370303,30,0.03389306059197376,0.09413492140710862,0.044578856460581115
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,pixel_max_abs,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.990234375,0.7890787760416667,0.8707170722698692,0.6896809895833333,0.8828125,0.9384765625,0.6196280311724905,pixel_max_abs,0.435546875,30,0.6004638671875,0.94091796875,0.1888034494224353
-int8_convrot,1.0,0.7360188802083333,0.8275553385416666,0.6247788134414806,0.8291015625,0.90625,0.564453125,pixel_max_abs,0.32763671875,30,0.53955078125,0.91796875,0.22603839516476007
-mxfp8,0.984375,0.7773111979166667,0.85859375,0.65722953895906,0.873046875,0.91015625,0.6752514523737252,pixel_max_abs,0.2666015625,30,0.68701171875,0.92724609375,0.21088690152917955
-nvfp4,1.0,0.84287109375,0.9004115576887538,0.7469826704145809,0.9140625,0.931640625,0.7919921875,pixel_max_abs,0.44091796875,30,0.77294921875,0.94580078125,0.1528960264293414
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,pixel_mse,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.046511166947092286,0.010874053185823435,0.018897392390576036,0.006073528938805388,0.007374139768811962,0.010979984319647914,0.0008209186663342537,pixel_mse,4.59174526192691e-05,30,0.000852767009862472,0.013948382328004525,0.012795916764529496
-int8_convrot,0.03842447766786728,0.006120242625723652,0.012077904882946732,0.0033165517645550096,0.0020879989336654303,0.00588155706080402,0.0003753197210690483,pixel_mse,2.1041879600397727e-05,30,0.0002952857566630708,0.00718952757875772,0.009096091239460636
-mxfp8,0.04611362986137035,0.008537968649407087,0.015781269222229083,0.004800823576657699,0.005077824795080722,0.007273513210324675,0.0005246900834412403,pixel_mse,7.53721164840869e-05,30,0.0005367923152941142,0.010257589766284816,0.011171156109163626
-nvfp4,0.08448238408001447,0.020066265895179992,0.02831380751125302,0.013346904111457857,0.01676510825490242,0.022131470451559748,0.004656167423765448,pixel_mse,0.00015960568907131406,30,0.004101960813450619,0.030228655747281733,0.018740534296128174
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,pixel_rmse,0.0,30,0.0,0.0,0.0
-fp8_scaled,0.21566447771270142,0.08551863220535331,0.11758913360271901,0.0592063756264977,0.08585596062258541,0.10460753085335903,0.028651678246382945,pixel_rmse,0.006776241776919497,30,0.029187427565051216,0.11802769274312241,0.06069099289716499
-int8_convrot,0.19602162551072594,0.0605931022400491,0.08686368291729023,0.04202902235871126,0.045686307281696814,0.07669131020398608,0.01937317013472623,pixel_rmse,0.004587142858076008,30,0.01713523582174047,0.08474941534403135,0.05033047941957317
-mxfp8,0.21474084348667896,0.07425315291442554,0.10354840216457115,0.05132281175100078,0.07125237252473124,0.08511686264605975,0.022690213764762948,pixel_rmse,0.008681711610280942,30,0.023115811369805367,0.10109378242848045,0.05593504174339372
-nvfp4,0.2906585351920953,0.12526690155312523,0.15344978320277544,0.095818878970696,0.129479922655383,0.1487664964014403,0.07308798585884464,pixel_rmse,0.01263351451779409,30,0.06365347923449301,0.17380182099892122,0.06727044699127928
 bf16,1.0,1.0,1.0,1.0,1.0,1.0,1.0,ssim_luminance,1.0,30,1.0,1.0,0.0
-fp8_scaled,0.9947213409839686,0.8495422321157533,0.905020318994351,0.7707973957272686,0.8714935187035986,0.9598562235679247,0.8041937490377443,ssim_luminance,0.5374379639887356,30,0.8035385577103054,0.9663904945389548,0.13608721815065564
-int8_convrot,0.9972517586996246,0.9111058857313673,0.9453379232272078,0.8513900470091572,0.9482424712836082,0.9778833355656921,0.876726818935421,ssim_luminance,0.5770211169880319,30,0.8709708878893548,0.986583532170725,0.09798492932033326
-mxfp8,0.9945743645380596,0.8730140327040307,0.9212629492203315,0.8008845404695334,0.9013037337013577,0.9759980211249115,0.8475112634561334,ssim_luminance,0.5457123650284678,30,0.8394579106397722,0.9817830528659783,0.12381098549546848
-nvfp4,0.9906649380505849,0.7731607227537941,0.8396720061366674,0.7057559784900083,0.7997759755317175,0.8746571129294994,0.6444890235190676,ssim_luminance,0.5126984077941714,30,0.6430643514080403,0.8805074212911161,0.149463063738976
 bf16,1.0,1.0,1.0,1.0,1.0,1.0,1.0,ssim_rgb,1.0,30,1.0,1.0,0.0
-fp8_scaled,0.9919694066047668,0.8482623716195424,0.9050848768837296,0.768556985410857,0.8743238747119904,0.9592909216880798,0.7988818287849426,ssim_rgb,0.5347716212272644,30,0.7983435392379761,0.9660519659519196,0.1373303919003205
-int8_convrot,0.9962493777275085,0.9105293810367584,0.9445882046916266,0.8507886751492683,0.9480270147323608,0.9772445559501648,0.8792413771152496,ssim_rgb,0.5750747323036194,30,0.8703338503837585,0.9863479137420654,0.09835773558757431
-mxfp8,0.9942013621330261,0.8716254810492198,0.919912446078432,0.7979284875054472,0.9016529023647308,0.9762930870056152,0.8465492129325867,ssim_rgb,0.5429816842079163,30,0.8388548493385315,0.980877161026001,0.12433808969369654
-nvfp4,0.985677182674408,0.770771857102712,0.836969576814614,0.703291576268642,0.7963234782218933,0.8719966411590576,0.6420286297798157,ssim_rgb,0.5119650363922119,30,0.6388882994651794,0.8811187148094177,0.14975735526647088

 int8_convrot,0.2126582463582357,0.015050061543782555,0.047096149377796305,0.0033317442824674446,0.0014796257019042969,0.004377841949462891,1.5417734781901043e-05,artifact_channel_clipping_fraction,0.0,30,1.2318293253580729e-05,0.0057706038157145185,0.04100993101200461
 mxfp8,0.16462389628092447,0.013665729098849826,0.03840960204540209,0.0032841261811778857,0.0016050338745117188,0.003711064656575521,3.9577484130859375e-05,artifact_channel_clipping_fraction,0.0,30,3.949801127115886e-05,0.004233121871948242,0.03333400564508893
 nvfp4,0.14287535349527994,0.010600037044949,0.03136560691746088,0.002320763345189201,0.0006882349650065105,0.0029071172078450522,6.421407063802083e-05,artifact_channel_clipping_fraction,0.0,30,7.001558939615885e-05,0.0039368470509847,0.027980738723959432
+int4_convrot,0.26186593373616535,0.026480144924587667,0.059449437902854244,0.010908189550362952,0.0023954709370930987,0.02277819315592448,0.0006526311238606771,artifact_channel_clipping_fraction,0.0,30,0.0003686745961507162,0.037626584370930985,0.05243294891036635
+gguf_q8_0,0.2151657740275065,0.015296501583523218,0.04613732539989189,0.0035051263744858914,0.00157928466796875,0.00476837158203125,1.7801920572916668e-05,artifact_channel_clipping_fraction,0.0,30,1.5894571940104168e-05,0.005908489227294922,0.04151592403483794
+gguf_q4_k_m,0.20561631520589194,0.014829391903347443,0.04808224814226402,0.00307183318982089,0.0011846224466959636,0.003157933553059896,1.71661376953125e-05,artifact_channel_clipping_fraction,0.0,30,1.3192494710286457e-05,0.005620082219441732,0.04009173345880691
+bf16,0.19443988800048828,0.011464087168375652,0.041368298741961164,0.002037938435872396,0.0,0.0,0.0,artifact_clip_black_fraction,0.0,30,0.0,0.0012514591217041016,0.036658314781696795
+fp8_scaled,0.20594120025634766,0.011704953511555989,0.04134731767610214,0.0018105189005533854,0.0,0.0,0.0,artifact_clip_black_fraction,0.0,30,0.0,0.0005252361297607422,0.038679964089007875
+int8_convrot,0.18839454650878906,0.011017163594563803,0.040165324273362654,0.0017235169433023466,0.0,0.0,0.0,artifact_clip_black_fraction,0.0,30,0.0,0.0010867118835449219,0.03552321994511315
+mxfp8,0.1392688751220703,0.009522501627604167,0.032189202106036444,0.0015846888224283855,9.5367431640625e-07,0.0005860328674316406,0.0,artifact_clip_black_fraction,0.0,30,0.0,0.0009281635284423828,0.027390805397619043
+nvfp4,0.11723518371582031,0.007248051961263021,0.026366080445690145,0.001183469453564453,5.7220458984375e-06,0.000217437744140625,0.0,artifact_clip_black_fraction,0.0,30,0.0,0.0008044242858886719,0.022386268607707725
+int4_convrot,0.2502403259277344,0.01315771738688151,0.052299593640074644,0.001724089294636818,4.4345855712890625e-05,0.0011606216430664062,0.0,artifact_clip_black_fraction,0.0,30,0.0,0.0032930374145507812,0.04634182895475138
+gguf_q8_0,0.18766307830810547,0.011172167460123698,0.04044095469551731,0.0017479578653971353,0.0,0.0,0.0,artifact_clip_black_fraction,0.0,30,0.0,0.001444101333618164,0.03547184118725842
+gguf_q4_k_m,0.18765544891357422,0.010993131001790364,0.041573060670829,0.001392809549967448,0.0,0.0,0.0,artifact_clip_black_fraction,0.0,30,0.0,0.0007436275482177734,0.03550407527732055
+bf16,0.0014410018920898438,6.335576375325521e-05,0.00029176105640316304,2.7338663736979167e-06,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,1.430511474609375e-06,0.0002654025730854578
 fp8_scaled,0.000102996826171875,9.409586588541667e-06,2.7942657470703126e-05,2.86102294921875e-07,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,0.0,2.539999995997267e-05
+int8_convrot,0.000896453857421875,4.4123331705729166e-05,0.00019818549014081246,2.6702880859375e-06,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,0.0,0.00016876680268736643
+mxfp8,0.001796722412109375,7.448196411132812e-05,0.00035429420193575323,3.337860107421875e-06,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,2.86102294921875e-06,0.0003299410262263989
+nvfp4,0.0004730224609375,2.5177001953125e-05,8.878707885742187e-05,5.753835042317708e-06,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,1.0967254638671875e-05,8.7420620015132e-05
+int4_convrot,0.035106658935546875,0.0017352104187011719,0.00813668200757779,8.907318115234376e-05,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,0.0002651214599609375,0.006717988535488141
+gguf_q8_0,0.0006732940673828125,3.118515014648438e-05,0.00014168421427408854,2.09808349609375e-06,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,0.0,0.00012471416633379575
+gguf_q4_k_m,2.86102294921875e-05,1.8755594889322916e-06,8.265177408854166e-06,1.5894571940104166e-07,0.0,0.0,0.0,artifact_clip_white_fraction,0.0,30,0.0,0.0,6.243538999233916e-06
+bf16,0.0026324703358113766,0.0012742011866066604,0.0015315434713544212,0.0011035637390884707,0.0011953407083638012,0.0013988425489515066,0.0010577477514743805,artifact_flat_region_chroma_noise,0.0003196971374563873,30,0.0010434366413392127,0.0014493349590338767,0.0004371060700129983
+fp8_scaled,0.002659283112734556,0.0012697198738654454,0.001533499787711189,0.0010946365929081498,0.001191404415294528,0.0013635556679219007,0.0010579403024166822,artifact_flat_region_chroma_noise,0.00026580505073070526,30,0.0010307297634426504,0.001444745052140206,0.00044712446368358726
+int8_convrot,0.0026403230149298906,0.00127472765937758,0.0015426125738150808,0.0011044190849891198,0.0011949676554650068,0.001417321851477027,0.0010504998499527574,artifact_flat_region_chroma_noise,0.0003394151572138071,30,0.0010300807771272957,0.0014406022382900119,0.00043755561264026866
+mxfp8,0.0026044989936053753,0.0012747316640646508,0.0015383891263355837,0.0011186470336085014,0.001196873839944601,0.0013951604487374425,0.0010489965206943452,artifact_flat_region_chroma_noise,0.0005627128994092345,30,0.0010335321421734989,0.0014184775936882943,0.0004112971785737646
+nvfp4,0.002881864085793495,0.0013037522012988726,0.0016481062563694616,0.0011368626259220208,0.0011877098586410284,0.0014002487296238542,0.0010488347616046667,artifact_flat_region_chroma_noise,0.0006382025312632322,30,0.0010546765406616032,0.0014527786406688392,0.0004666635390877322
+int4_convrot,0.0025616937782615423,0.0013227589071902913,0.001580132607749644,0.0011353802129039528,0.0012644125963561237,0.001393658109009266,0.0011459568631835282,artifact_flat_region_chroma_noise,8.796228939900175e-05,30,0.00114232485066168,0.0014032410690560937,0.00045890538713973966
+gguf_q8_0,0.0026272067334502935,0.0012748295344257106,0.0015355096383766365,0.0011045966919497816,0.0011971559724770486,0.0014271748950704932,0.001049526035785675,artifact_flat_region_chroma_noise,0.00035556743387132883,30,0.0010317956330254674,0.00145500194048509,0.0004356131711188145
+gguf_q4_k_m,0.0028456319123506546,0.001285409289994277,0.0016088475340613553,0.0011087680475652575,0.001207761000841856,0.0014278074959293008,0.00105609797174111,artifact_flat_region_chroma_noise,0.0003337148518767208,30,0.0010407670924905688,0.0014874003827571869,0.0004798912232978273
+bf16,6.416837584264616,5.630332298254742,5.937614509856135,5.126718445388891,5.934040832522571,6.146767374611394,5.258676736978931,artifact_flat_region_gradient_entropy_bits,2.081308045850826,30,5.2682502854271425,6.192119795427443,0.9011274075403761
+fp8_scaled,6.333523352792442,5.601924281404789,5.928075478904051,5.064588727469621,6.004031895442508,6.147973108204308,5.3368821121190475,artifact_flat_region_gradient_entropy_bits,1.78379743857564,30,5.319295131006731,6.15346466603812,0.960667470081641
+int8_convrot,6.389288099922436,5.634416491318564,5.935766126663895,5.154456687541284,5.938448086535914,6.120962602204186,5.304770523106146,artifact_flat_region_gradient_entropy_bits,2.179185380146164,30,5.2832052790499935,6.195056174030415,0.8868561651273056
+mxfp8,6.336303336115902,5.6826921400618495,5.957828277844085,5.292319715110492,5.953401821241342,6.132035892335772,5.269578304671067,artifact_flat_region_gradient_entropy_bits,3.3418166934141413,30,5.248905689176285,6.168361318297629,0.7313146335757138
+nvfp4,6.286260143740057,5.680537621693147,5.963327795622333,5.242768872886233,6.008914027761253,6.172456878839885,5.4325163153973755,artifact_flat_region_gradient_entropy_bits,3.67290958965281,30,5.439807557277379,6.178694902309257,0.744424752329161
+int4_convrot,6.338415153460364,5.616530921666976,5.943207059472356,4.948039249906836,6.040007728133345,6.118968563235898,5.574135653645484,artifact_flat_region_gradient_entropy_bits,0.7022671443860338,30,5.516848322556719,6.162392086143196,1.087129798735776
+gguf_q8_0,6.3640889553443145,5.638662680874814,5.942652183248381,5.155804852859653,5.959067539517573,6.130025177508605,5.263281248897789,artifact_flat_region_gradient_entropy_bits,2.255960171394735,30,5.270419885831353,6.2025168606475845,0.8763247525451567
+gguf_q4_k_m,6.405489981343903,5.6284024471337295,5.96647912091978,5.078938917117719,6.061517041419065,6.192686024011687,5.280603967258427,artifact_flat_region_gradient_entropy_bits,2.306838906404827,30,5.299453659991719,6.214560628590451,0.9367132814285739
+bf16,0.7955513000488281,0.13440872206124965,0.23732791642542567,0.07012191621787173,0.05450516607505913,0.17257664881716345,0.017265583276112002,artifact_flat_region_near_zero_gradient_fraction,0.006924018906577246,30,0.018747927625392624,0.19160499226559563,0.18103009860272135
+fp8_scaled,0.8287536669756654,0.13920717341054653,0.24609957014642472,0.07314473806600233,0.05563413500106798,0.17328643798828125,0.0213623046875,artifact_flat_region_near_zero_gradient_fraction,0.005779266357421875,30,0.02225401394762204,0.19564828211489171,0.18841612933729052
+int8_convrot,0.77838433778144,0.1329126708709066,0.23126104824672744,0.06785352412325732,0.05279434487081617,0.17612883221580558,0.014584756011490788,artifact_flat_region_near_zero_gradient_fraction,0.0068439585089784035,30,0.016851347770336842,0.1959620507691832,0.17905842696004376
+mxfp8,0.6211738586425781,0.12626263689575995,0.21421163648476207,0.06825828554236804,0.05425506154264905,0.19073940528431582,0.01610111530981465,artifact_flat_region_near_zero_gradient_fraction,0.007702050065232816,30,0.01814025146038833,0.1959309682312283,0.15595269837596937
+nvfp4,0.5716285705566406,0.12654183438988204,0.22021744060178286,0.06652512926964549,0.057048203474104664,0.1744071241149601,0.019208879857604486,artifact_flat_region_near_zero_gradient_fraction,0.003456313494500799,30,0.019502141765761958,0.17690198326911827,0.1581650255524848
+int4_convrot,0.947998046875,0.1296637405563471,0.25037779351246736,0.06673989944746832,0.056972710748777765,0.15752029418945312,0.01676273895677319,artifact_flat_region_near_zero_gradient_fraction,0.005229949951171875,30,0.01878619606328302,0.1729612769500613,0.19630725979442354
+gguf_q8_0,0.7726391845392051,0.13305466880800546,0.23410361951440128,0.06999506106177651,0.05083160603257776,0.171697525762893,0.018883615888178475,artifact_flat_region_near_zero_gradient_fraction,0.00664520263671875,30,0.020444564638301826,0.19033455221053416,0.17839644322129894
+gguf_q4_k_m,0.7728363413024487,0.13922929989758978,0.24930943343788545,0.06859118409152301,0.0543844311776515,0.16818349243377637,0.014741396083458277,artifact_flat_region_near_zero_gradient_fraction,0.00642419525891338,30,0.015113931278902301,0.18491640248803615,0.18821428152136122
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+gguf_q8_0,0.8275871276855469,0.47456475694974265,0.5853191427857007,0.365012702739316,0.4829190820455551,0.5671161115169525,0.2734183967113495,artifact_luminance_mean,0.12270329892635345,30,0.29170335084199905,0.6660377979278564,0.2223044209314004
+gguf_q4_k_m,0.8349631428718567,0.4686392615238825,0.5804270630639243,0.36068915937057455,0.478660523891449,0.5536926686763763,0.2686625123023987,artifact_luminance_mean,0.11951406300067902,30,0.2874821722507477,0.6539942920207977,0.22119087309912783
+bf16,0.29011160135269165,0.21992382928729057,0.24338478096525684,0.18369106430764195,0.24474745988845825,0.25892728567123413,0.1800270974636078,artifact_luminance_std,0.07326649874448776,30,0.1816285066306591,0.2615939676761627,0.05855414473314542
+fp8_scaled,0.293201208114624,0.220692940056324,0.2437686786905207,0.18439023410427105,0.24421841651201248,0.25936856865882874,0.18172059953212738,artifact_luminance_std,0.07360288500785828,30,0.18372415378689766,0.26235271245241165,0.058123737993796436
+int8_convrot,0.2897592782974243,0.22030811284979185,0.2435378264585187,0.18406782781742922,0.24509183317422867,0.2589303106069565,0.1781918704509735,artifact_luminance_std,0.07315973937511444,30,0.18125183880329132,0.26139361411333084,0.0586937316382405
+mxfp8,0.29982978105545044,0.22149013926585515,0.24548043596442287,0.1847547937114461,0.2470528706908226,0.2577151358127594,0.17928209900856018,artifact_luminance_std,0.0733739584684372,30,0.18124116584658623,0.26193416863679886,0.05967059698460225
+nvfp4,0.28884589672088623,0.21807041441400846,0.23992520120636127,0.18191302501344855,0.23719629645347595,0.2506242170929909,0.18501295149326324,artifact_luminance_std,0.07255903631448746,30,0.1872648522257805,0.25252117961645126,0.055867771197170416
+int4_convrot,0.3059524893760681,0.22635182390610378,0.2497052281058263,0.19160771176065036,0.2501652091741562,0.2598147988319397,0.19474434852600098,artifact_luminance_std,0.0798766016960144,30,0.19777939841151237,0.2616719380021095,0.05760632831356761
+gguf_q8_0,0.29463905096054077,0.2199059230585893,0.24389052072093304,0.18397595287918408,0.2445196807384491,0.25902682542800903,0.18055520951747894,artifact_luminance_std,0.07341570407152176,30,0.1829400509595871,0.261923186480999,0.05902985628256024
+gguf_q4_k_m,0.2900227904319763,0.218133195489645,0.24238954146499975,0.18147827704668573,0.24350182712078094,0.2608356326818466,0.1679830104112625,artifact_luminance_std,0.06974280625581741,30,0.1708824709057808,0.261458158493042,0.060295940438234885
+bf16,0.34344542026519775,0.11398565527051688,0.16696308684822603,0.07771300006242284,0.10549644008278847,0.14687331020832062,0.04665585793554783,artifact_saturation_mean,0.008430104702711105,30,0.04829507227987051,0.16222890466451645,0.08755793359108803
+fp8_scaled,0.34116125106811523,0.11448063325757782,0.16768593572547466,0.07760366802414773,0.10298968479037285,0.14753016829490662,0.045853324234485626,artifact_saturation_mean,0.00894435029476881,30,0.047263165935873985,0.1612839177250862,0.08896540188546719
+int8_convrot,0.34348899126052856,0.11391610754653811,0.1666612907931083,0.07733725022017388,0.10668217018246651,0.14877855777740479,0.04557467997074127,artifact_saturation_mean,0.008413145318627357,30,0.04681415855884552,0.16164465248584747,0.08733128433565733
+mxfp8,0.34628283977508545,0.11385334435229501,0.16802519049190143,0.07739906067396624,0.10478852689266205,0.1456046849489212,0.04750455170869827,artifact_saturation_mean,0.009984525851905346,30,0.04804812744259834,0.1575765237212181,0.08859439675565352
+nvfp4,0.33353155851364136,0.11345184243594607,0.1655775372575092,0.0772886519864879,0.11028828471899033,0.13931508362293243,0.045858390629291534,artifact_saturation_mean,0.0062842704355716705,30,0.047630670480430126,0.15444738790392876,0.08686385198765167
+int4_convrot,0.3071558475494385,0.11184075785179932,0.15920755588291252,0.07872708669026071,0.10528886318206787,0.135818712413311,0.05382675677537918,artifact_saturation_mean,0.011135261505842209,30,0.05300867650657892,0.14621388539671898,0.0787107043861786
+gguf_q8_0,0.3433770537376404,0.11396450434500972,0.16822325180400108,0.07771461461124589,0.10565118491649628,0.1462012082338333,0.04757782816886902,artifact_saturation_mean,0.008971265517175198,30,0.04831772856414318,0.16119711101055145,0.08809218125720855
+gguf_q4_k_m,0.3525537848472595,0.11171482006708781,0.16617374907250057,0.07528712737293067,0.0994982197880745,0.13324643671512604,0.04594610631465912,artifact_saturation_mean,0.008324021473526955,30,0.046329863369464874,0.15637607872486115,0.08925505369411138
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,delta_e_2000_mean,0.0,30,0.0,0.0,0.0
+fp8_scaled,11.483121871948242,3.966377484798431,5.887792585883809,2.573259945478504,2.5886929035186768,4.748284816741943,1.3150166273117065,delta_e_2000_mean,0.42607879638671875,30,1.2700645327568054,4.9170098304748535,3.3691278175130255
+int8_convrot,9.37054443359375,2.3873159204920134,3.697525820513594,1.6053033537811214,1.234503149986267,2.6648285388946533,0.6395715475082397,delta_e_2000_mean,0.23851720988750458,30,0.6368480324745178,3.561531722545624,2.2612009545558043
+mxfp8,10.63775634765625,3.3460651487112045,4.956765392325992,2.2204938924048565,2.6430084705352783,3.6377205848693848,0.9271849393844604,delta_e_2000_mean,0.44776690006256104,30,0.9909160435199738,4.94148850440979,2.8674949537710908
+nvfp4,19.292980194091797,6.659375993410746,8.47782459714621,5.03495832873238,6.313443660736084,8.464838981628418,3.4050838947296143,delta_e_2000_mean,0.7552700042724609,30,3.1281118392944336,9.676307439804077,4.2145226468747214
+int4_convrot,24.21453094482422,10.267134523391723,13.635003135455003,7.6227921267134615,9.150108337402344,12.856962203979492,5.294098377227783,delta_e_2000_mean,1.3954126834869385,30,5.111790657043457,13.351920127868652,6.281755222751914
+gguf_q8_0,9.947036743164062,2.3953464051087696,3.8271838812035424,1.5427439046065117,1.862764835357666,2.8841936588287354,0.7818159461021423,delta_e_2000_mean,0.22240394353866577,30,0.6313264071941376,3.2199647426605225,2.331150896568802
+gguf_q4_k_m,14.697423934936523,5.656645333766937,7.340707254998892,4.01791214310432,5.339750289916992,7.664797782897949,2.6376092433929443,delta_e_2000_mean,0.6819735765457153,30,2.491503417491913,8.239952564239502,3.638060617631283
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,delta_e_2000_p95,0.0,30,0.0,0.0,0.0
+fp8_scaled,60.031036376953125,17.211933064460755,25.901402774038253,10.639616981734177,11.545272827148438,21.604257583618164,4.131968021392822,delta_e_2000_p95,1.0401486158370972,30,4.10766863822937,25.547958374023438,16.98805428704481
+int8_convrot,60.92300796508789,11.434780442714692,18.447402891931173,6.689456756203022,4.601085424423218,11.373390674591064,1.6462477445602417,delta_e_2000_p95,0.5516780614852905,30,1.6362426578998566,14.727276802062988,14.514765819904927
+mxfp8,60.938411712646484,15.235675468047459,22.915436998729355,9.587153557405967,11.239513874053955,18.10616683959961,3.312631845474243,delta_e_2000_p95,0.7117034792900085,30,3.1227405667304993,20.36545753479004,15.725032370916187
+nvfp4,61.69163513183594,27.125506460666656,34.61912026801719,19.959955988980607,28.628643035888672,33.81364822387695,12.64588451385498,delta_e_2000_p95,1.7622984647750854,30,12.146420001983643,36.15382385253906,17.380972841306836
+int4_convrot,71.36679077148438,35.17800121307373,45.34740610582393,25.978001685070623,32.64629936218262,47.97770690917969,16.7833309173584,delta_e_2000_p95,3.4165215492248535,30,15.341195106506348,48.65043640136719,20.145533572392875
+gguf_q8_0,50.0253791809082,11.235154736042023,18.3577128150845,6.42395185247403,6.505018949508667,11.220423221588135,1.775044560432434,delta_e_2000_p95,0.53924560546875,30,1.7028608918190002,13.635668516159058,13.529021417607778
+gguf_q4_k_m,62.333892822265625,22.869729046026865,30.12990367256274,15.547161075256417,23.28391456604004,31.25609302520752,7.517449378967285,delta_e_2000_p95,1.3479758501052856,30,7.308003902435303,33.111852169036865,17.123298183086835
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,edge_mae,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.33001643419265747,0.07532161632552743,0.13930944568898426,0.04544289823106702,0.04912411421537399,0.0703529343008995,0.022591352462768555,edge_mae,0.0046882363967597485,30,0.021705199498683214,0.08493299782276154,0.08448712712844918
+int8_convrot,0.3080732226371765,0.05368041183489065,0.10695981163220979,0.030531610619935146,0.026883888989686966,0.04516151547431946,0.013133929576724768,edge_mae,0.0034100841730833054,30,0.012441205559298396,0.05733771622180939,0.07038598169635805
+mxfp8,0.30713704228401184,0.06807803912088276,0.12814387849338352,0.04004108847891134,0.04082764685153961,0.0697740912437439,0.018377656117081642,edge_mae,0.004183486104011536,30,0.018760444596409798,0.0701916553080082,0.08038695349965973
+nvfp4,0.35786503553390503,0.09647003185624878,0.15483449089777032,0.06345773019514597,0.0685483030974865,0.10237939655780792,0.03989491984248161,edge_mae,0.005974304862320423,30,0.03622992895543575,0.11117053590714931,0.0889229575086993
+int4_convrot,0.3905501365661621,0.11340232094128927,0.17996652714610817,0.07650563967501191,0.09093757346272469,0.11818301305174828,0.04553857073187828,edge_mae,0.008837084285914898,30,0.046125506050884724,0.1272311583161354,0.09772767409600709
+gguf_q8_0,0.31240805983543396,0.05398907176374147,0.1073184148380044,0.030537550662396794,0.027585715055465698,0.05935930833220482,0.012097450904548168,edge_mae,0.0032236482948064804,30,0.011087994556874037,0.06009658984839916,0.06980598636659784
+gguf_q4_k_m,0.32147520780563354,0.09083299962803722,0.14735934505032453,0.05742135074146099,0.05791335366666317,0.09847811236977577,0.03130633756518364,edge_mae,0.005259406752884388,30,0.03156483732163906,0.10800106450915337,0.08617770943787374
 bf16,1.0000000000000002,1.0,0.9999999999999999,0.9999999999999999,1.0,0.9999999999999998,0.9999999999999998,final_latent_cosine,0.9999999999999998,30,1.0,1.0000000000000002,1.6748780794631286e-16
+fp8_scaled,0.9990132257394989,0.95768181308319,0.9758015967812483,0.9320701545133961,0.974916844990766,0.9918454746848921,0.9381547628183944,final_latent_cosine,0.8440704265372225,30,0.9322940696767154,0.9947484577846772,0.0457573571854162
+int8_convrot,0.9996738629611984,0.9794700450363916,0.9880697196571017,0.9687830959453048,0.9911894084839659,0.996725111071858,0.9718072555648413,final_latent_cosine,0.9143871625882631,30,0.9675368644940532,0.9981169210243355,0.024724095004720455
+mxfp8,0.9997066815818643,0.9669332757282731,0.9809209349843624,0.9493300047550072,0.9796811619102106,0.9974308978066833,0.9536419144488646,final_latent_cosine,0.8904270511857791,30,0.9432470155029122,0.9977857851759111,0.03638925130596308
+nvfp4,0.997225161488146,0.9169226004348068,0.9430073738776198,0.8870893496791321,0.9268460079540761,0.9646389444149731,0.8799963143377532,final_latent_cosine,0.6775479449230533,30,0.8705420260770691,0.9741240532265472,0.07239929399554912
+int4_convrot,0.9942978377811718,0.8568672418596085,0.9053446369823137,0.7923442905925553,0.8799561457916107,0.9588744477501203,0.812125658351456,final_latent_cosine,0.5797442112408234,30,0.8029939583040128,0.9618171494830972,0.11895712471807912
+gguf_q8_0,0.9998941607419957,0.9791950350136175,0.9886014185287781,0.9661664053731657,0.9894186168284973,0.9984581787120179,0.9720523338401579,final_latent_cosine,0.9101157105713021,30,0.9667533005227373,0.9990276717041361,0.025461362100022825
+gguf_q4_k_m,0.9981401529920513,0.9352926861711494,0.9586248535655357,0.909468213440802,0.9413276251567192,0.9798217867390568,0.9020750026096842,final_latent_cosine,0.7813621720442112,30,0.8964234869991148,0.9828233163784796,0.056263771827828386
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_mae,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.43088059438190385,0.16253725938964295,0.22715095437524913,0.11321061010565234,0.14401144072247973,0.21486968391621275,0.0632440157585279,final_latent_mae,0.0344473923613009,30,0.0590217649642355,0.22904379459394875,0.11869481578535535
+int8_convrot,0.34459674727068546,0.09785262226524007,0.13963517745498366,0.06740705046487842,0.061628942384125196,0.12062073918534111,0.0372752012475317,final_latent_mae,0.01399631493463005,30,0.03373658026252713,0.1379440010756241,0.084615045960451
+mxfp8,0.42524115449811006,0.1403025753338118,0.19699613292580115,0.09696245674126595,0.11666390966887263,0.1718260712149089,0.046522255345173846,final_latent_mae,0.019534103462888197,30,0.04567510139770903,0.20301869442146625,0.10845983560431778
+nvfp4,0.5718134701574229,0.26427991591832456,0.3139038223244819,0.20841331566114615,0.26637238939211727,0.35794007712409837,0.1643270599488389,final_latent_mae,0.06821991900285695,30,0.14772310937533106,0.3591660619070538,0.12819963476138754
+int4_convrot,0.9256390626047448,0.39127930287687795,0.4891188569047702,0.30725895976115214,0.3724110698090328,0.48519457055221693,0.21330140454653446,final_latent_mae,0.11867898339986027,30,0.20917506493182358,0.508577043020999,0.1939872803869709
+gguf_q8_0,0.3762036819663308,0.09737802843484218,0.14553122744565838,0.06540253486914249,0.0720067853011841,0.1285228941222485,0.028097455035144492,final_latent_mae,0.014520777250524475,30,0.02562061307255803,0.1541437467128155,0.08882674189409348
+gguf_q4_k_m,0.47341706981895015,0.22653762967202104,0.27878303939289395,0.17375335386811994,0.21923339227981842,0.30025952487883956,0.12343858348850745,final_latent_mae,0.0580937166544544,30,0.12214031422626448,0.31285614405080153,0.1186786086377116
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_max_abs,0.0,30,0.0,0.0,0.0
+fp8_scaled,6.810203313827515,3.4788162966569263,4.097296363331234,2.798106687790829,3.94640189409256,4.331535577774048,2.2962564826011658,final_latent_max_abs,1.1445398926734924,30,2.2158609330654144,4.389311924576759,1.4032367657749338
+int8_convrot,6.822402715682983,3.0971722836295763,3.682247978961108,2.467355995287729,3.4214413315057755,3.803535223007202,2.2851668000221252,final_latent_max_abs,0.8671616911888123,30,2.1549638360738754,3.8457661867141724,1.4207052177754584
+mxfp8,6.87958025932312,3.235787672797839,3.815696778941891,2.5642663800002206,3.606418550014496,4.035384714603424,2.1296051666140556,final_latent_max_abs,1.0756072998046875,30,2.1120776124298573,4.108494818210602,1.3975605219273086
+nvfp4,7.554713249206543,4.248068203032017,4.906162958036151,3.5443328441428514,4.420087456703186,4.746770858764648,3.782839208841324,final_latent_max_abs,1.5216014385223389,30,3.7823924124240875,4.86954802274704,1.4433200460136069
+int4_convrot,7.238463401794434,4.719983108341694,5.335783853105764,3.9879637545622937,4.894056797027588,5.422322511672974,3.9202542901039124,final_latent_max_abs,1.6942468285560608,30,4.001815557479858,5.626847267150879,1.382328329598143
+gguf_q8_0,6.812732458114624,3.0068762322266895,3.555599342684809,2.374542998980838,3.396212935447693,3.844132900238037,1.5516021251678467,final_latent_max_abs,0.5595033764839172,30,1.4381099045276642,3.9919084310531616,1.50100610740487
+gguf_q4_k_m,6.930643081665039,3.9185338402787844,4.4638349068493595,3.2391134315918357,4.0794040858745575,4.752050161361694,3.3161949813365936,final_latent_max_abs,1.2339977025985718,30,3.12244476377964,4.866068780422211,1.3370910805422387
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 fp8_scaled,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 int8_convrot,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 mxfp8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 nvfp4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
+int4_convrot,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
+gguf_q8_0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
+gguf_q4_k_m,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_nonfinite_count,0.0,30,0.0,0.0,0.0
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_relative_l2,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.561867670817564,0.24611432114340853,0.32696396301732966,0.17604227412491427,0.22361785798622946,0.3219189353695148,0.10801230172947905,final_latent_relative_l2,0.04513061589334775,30,0.102422355026235,0.3656132232745428,0.15942733762085928
+int8_convrot,0.4117376784326574,0.16570607353080016,0.21651452013016817,0.11671402921201494,0.13257123231998846,0.22416861101912317,0.0627901055265302,final_latent_relative_l2,0.025538385579491816,30,0.06153399646256064,0.25466554422273574,0.11833088525091065
+mxfp8,0.4726395015778749,0.2139005903192808,0.28148279953520944,0.14956514729315284,0.2018998584626961,0.2638497962771371,0.06504099210841373,final_latent_relative_l2,0.024725070144718423,30,0.06670065480556368,0.3338293852941963,0.14614951286698435
+nvfp4,0.7917400959936337,0.3659206445697244,0.436471044439881,0.28650029461980436,0.3813605818588721,0.4402367225121656,0.248114629784772,final_latent_relative_l2,0.07451581635116171,30,0.2262810394511847,0.504546459842452,0.17796772156389645
+int4_convrot,0.9227312819644936,0.4890777530246518,0.6014069540833724,0.3815084684474934,0.4908681550551591,0.6100007128966033,0.2787427363787042,final_latent_relative_l2,0.10665512765983909,30,0.2756452002924681,0.6293111864258787,0.2297482326723181
+gguf_q8_0,0.42728122762362286,0.16393504464305175,0.22345513884101062,0.11495066044641478,0.14568786445950718,0.2143112544891126,0.049409051936206635,final_latent_relative_l2,0.014568130156638937,30,0.04386807552129296,0.2580509126138479,0.1237939272566236
+gguf_q4_k_m,0.6524379262376564,0.3214413120784904,0.39422056465558014,0.24454299011582564,0.3406162532589876,0.4323342302220082,0.20304566240569139,final_latent_relative_l2,0.06097064346235551,30,0.20002878821824743,0.45595169537672436,0.16318124270193038
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,final_latent_rmse,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.7377951901563011,0.2997290529280748,0.4022459704916589,0.21206202939269,0.27677320547453743,0.3869242548141378,0.11625437725379603,final_latent_rmse,0.05394278418661119,30,0.11010036654942262,0.4128623872869279,0.20276907999996566
+int8_convrot,0.7580860692740139,0.2053203749685123,0.2839154881727243,0.14371563045293265,0.1698773768622126,0.24011047247945894,0.07045042082196983,final_latent_rmse,0.029828283805020377,30,0.06929466972765312,0.2893305781935036,0.1687018116212049
+mxfp8,0.7843178276372679,0.2597711335533564,0.3525011325362048,0.1821404723873839,0.22786624558258123,0.29046383484470384,0.0874775293548552,final_latent_rmse,0.0359873609126252,30,0.08383933366756499,0.3597845159935686,0.193874026271923
+nvfp4,0.9401643557820462,0.43986440201573745,0.5321936476087231,0.3472321003586412,0.4359448089334799,0.5840122423774203,0.2859338339372067,final_latent_rmse,0.11212599804958107,30,0.24795566698553517,0.5929091233624896,0.21494028630006545
+int4_convrot,1.542125200772799,0.5967536484008615,0.7674184982553053,0.46523753435924126,0.5612961832335543,0.7488224454966103,0.3059412809557831,final_latent_rmse,0.16658225829951387,30,0.30358793519153926,0.7634363836381748,0.3129486995372591
+gguf_q8_0,0.7056793078399555,0.20093004999263106,0.2814116839765029,0.1398926389658149,0.16931596988120343,0.2153697879695872,0.05536208227988816,final_latent_rmse,0.02681345623388868,30,0.053082294630554624,0.27815297762228797,0.17224270622135318
+gguf_q4_k_m,0.7810297255896639,0.3854159512354935,0.46771375849321206,0.29542667478880824,0.3996497247636932,0.47827015564906183,0.21855690364827957,final_latent_rmse,0.09522868427223442,30,0.22019667734477066,0.5141323614627847,0.1929172769491478
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,luminance_histogram_js,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.33825114263452427,0.01945370763599984,0.06419561154879448,0.00652599941186096,0.00332612580094384,0.009885450498588884,0.0018722693641762779,luminance_histogram_js,0.0003761274468946634,30,0.0017676893450690196,0.012086350192003482,0.061153551829226126
+int8_convrot,0.2956382769436881,0.015576551361549248,0.06156914673581835,0.0031481184940420973,0.001579743705753635,0.0035642993122832678,0.0006561202953513216,luminance_histogram_js,0.00016029276910201263,30,0.0005123360598632729,0.004623427479476562,0.054033804935340435
+mxfp8,0.44879910510342297,0.03632349678009688,0.13192153369376958,0.00896494629482139,0.0023263563025843147,0.00572349126291466,0.001028710297362082,luminance_histogram_js,0.000311245546924982,30,0.000990672817108278,0.00839515983478614,0.09573689993782084
+nvfp4,0.2975578595152799,0.04419635920423148,0.08227298936966453,0.020849029726129863,0.012406239554403987,0.03667384142878227,0.006629875881504943,luminance_histogram_js,0.0026423989324661206,30,0.006316367910987301,0.04494439882821958,0.07304364824761002
+int4_convrot,0.42010054105036787,0.07652814052661103,0.16516641632394755,0.03696543003424891,0.018489322025153417,0.0559455453569553,0.010649411408757366,luminance_histogram_js,0.0040292838655711545,30,0.01021398374269627,0.07303051244622785,0.11766822122071828
+gguf_q8_0,0.25138216675930614,0.014654057279504579,0.04693086170751659,0.00472115319831668,0.001609429856380643,0.0035255798682941797,0.0005012685990991685,luminance_histogram_js,0.00011283360412319066,30,0.0004663616799831969,0.00603834052580851,0.046018251739517534
+gguf_q4_k_m,0.36436111233164126,0.04953146865956312,0.11928328290985944,0.01648602021461974,0.012259774049683345,0.02068615512718905,0.005158513420213879,luminance_histogram_js,0.00295501141167707,30,0.0050048307868443,0.029185822843959507,0.09355219437920609
 bf16,1.0000000000000002,1.0,1.0,1.0,1.0,0.9999999999999999,0.9999999999999999,pixel_cosine,0.9999999999999998,30,0.9999999999999999,1.0000000000000002,1.4723000231259014e-16
+fp8_scaled,0.9997174101836451,0.977177909784758,0.9879025784982869,0.963932187099867,0.9887227846325779,0.9982173979477897,0.9631982979465751,pixel_cosine,0.920929753854715,30,0.9613772315572804,0.9986170235867041,0.02546210798368741
+int8_convrot,0.9999191637627433,0.9886657918554602,0.993482300389649,0.9811533242725664,0.9953487300694817,0.9987661159080022,0.9851095801009797,pixel_cosine,0.9483690245205272,30,0.9806195740889176,0.9992983581745922,0.013768144113868737
+mxfp8,0.999908597658179,0.983809107885415,0.9906932696070647,0.9750051357077075,0.9867104253517991,0.9987344837314194,0.9737984215809845,pixel_cosine,0.9462661755937913,30,0.9732533670824269,0.9990265944457568,0.016655771843030468
+nvfp4,0.9993925868579964,0.9571523024286236,0.9735301526020309,0.9361149762926662,0.9703988935282456,0.9896606417752621,0.9321832728578532,pixel_cosine,0.8589949462434774,30,0.9313707413549633,0.9907761836811553,0.04152474355923369
+int4_convrot,0.9971954680960409,0.9262337503347184,0.9550056779400973,0.8868227144278665,0.9552145264345422,0.9833523518057155,0.9015752013748606,pixel_cosine,0.7247752992103546,30,0.9010896653952006,0.9835810375673184,0.07116705390677375
+gguf_q8_0,0.9999414432659867,0.9883925568678423,0.9936567569168349,0.9801678137884207,0.9947484196099656,0.9995033836897472,0.9825087566892895,pixel_cosine,0.9474815000141574,30,0.9821883607603519,0.9996922175537619,0.01381749638708261
+gguf_q4_k_m,0.9995382135549459,0.9612657248335854,0.9774404858111346,0.9257889463547536,0.9755657054418756,0.9938986872229765,0.9542060250782674,pixel_cosine,0.7877257482454687,30,0.9505312138842634,0.9947785215344808,0.047854772806016604
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,pixel_mae,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.12151041816029344,0.039640880672782756,0.05906300826028225,0.02614573828974821,0.030032288642511652,0.048271662036881935,0.014071846091686286,pixel_mae,0.0035665421485949387,30,0.012675566307128369,0.04856900256260133,0.033664231274267785
+int8_convrot,0.10591174032151685,0.02391713974267936,0.03888277777649966,0.016087678592611926,0.013695026543246058,0.028048601296935278,0.0065201610242032375,pixel_mae,0.0018026253486217076,30,0.006405253760211356,0.034092428643456664,0.02377175939197411
+mxfp8,0.12146794873660838,0.033667628081583544,0.051363375007687534,0.022587771551908317,0.02887947293072557,0.03703606275701077,0.00874234130272574,pixel_mae,0.003720539321875549,30,0.009623546750996564,0.045891529829380794,0.0296620873935028
+nvfp4,0.21235614602725641,0.06762622004486428,0.08660770580638986,0.0508139566504121,0.061716821261446356,0.08697530288956538,0.0345933651303009,pixel_mae,0.006045911217370303,30,0.03389306059197376,0.09413492140710862,0.044578856460581115
+int4_convrot,0.24330646071098272,0.10269503816140665,0.13634399872637204,0.07686891440598713,0.08755048082525718,0.13124022963294615,0.053372013998947906,pixel_mae,0.014090844700452484,30,0.0505897861877429,0.13952849795886854,0.062012287065229196
+gguf_q8_0,0.11332421623819755,0.023879413112813885,0.039969577478454504,0.01537696796126933,0.01833712969221324,0.028385645947423654,0.006900609470752292,pixel_mae,0.0015720843590922111,30,0.006141384129477956,0.03325665250105203,0.024452914857575614
+gguf_q4_k_m,0.16389446544530983,0.05739326747999359,0.07591082990466566,0.041206945676203525,0.05575683301515255,0.07329752542750612,0.027670659927177137,pixel_mae,0.00597069366449432,30,0.024590198471211504,0.07533340573801676,0.03839857369616954
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,pixel_max_abs,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.990234375,0.7890787760416667,0.8719401041666667,0.6928548177083333,0.8828125,0.939453125,0.631591796875,pixel_max_abs,0.435546875,30,0.6004638671875,0.94091796875,0.1888034494224353
+int8_convrot,1.0,0.7360188802083333,0.8275829904384466,0.623828125,0.8291015625,0.90625,0.564453125,pixel_max_abs,0.32763671875,30,0.53955078125,0.91796875,0.22603839516476007
+mxfp8,0.984375,0.7773111979166667,0.8583984375,0.6554505490397109,0.873046875,0.91015625,0.68359375,pixel_max_abs,0.2666015625,30,0.68701171875,0.92724609375,0.21088690152917955
+nvfp4,1.0,0.84287109375,0.901171875,0.7489094319317616,0.9140625,0.931640625,0.7919921875,pixel_max_abs,0.44091796875,30,0.77294921875,0.94580078125,0.1528960264293414
+int4_convrot,1.0,0.8854654947916667,0.9353515625,0.7909083909651501,0.9423828125,0.96484375,0.849609375,pixel_max_abs,0.470703125,30,0.8544921875,0.966796875,0.13634093347906132
+gguf_q8_0,1.0,0.7337117513020833,0.827616677135163,0.6241848868032336,0.8291015625,0.9169921875,0.5498046875,pixel_max_abs,0.33447265625,30,0.526123046875,0.92919921875,0.23005091199516395
+gguf_q4_k_m,0.994140625,0.8361002604166666,0.8948134158794552,0.7444151305046794,0.8984375,0.9384765625,0.8076171875,pixel_max_abs,0.41015625,30,0.80615234375,0.94677734375,0.15619086538700447
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,pixel_mse,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.046511166947092286,0.010874053185823435,0.0189599065061854,0.006095181723487172,0.007374139768811962,0.010979984319647914,0.0008209186663342537,pixel_mse,4.59174526192691e-05,30,0.000852767009862472,0.013948382328004525,0.012795916764529496
+int8_convrot,0.03842447766786728,0.006120242625723652,0.01217492815324518,0.0033275772128469536,0.0020879989336654303,0.00588155706080402,0.0003753197210690483,pixel_mse,2.1041879600397727e-05,30,0.0002952857566630708,0.00718952757875772,0.009096091239460636
+mxfp8,0.04611362986137035,0.008537968649407087,0.01585505533493698,0.004790083230145829,0.005077824795080722,0.007273513210324675,0.0005246900834412403,pixel_mse,7.53721164840869e-05,30,0.0005367923152941142,0.010257589766284816,0.011171156109163626
+nvfp4,0.08448238408001447,0.020066265895179992,0.028309935414635685,0.013388327566512932,0.01676510825490242,0.022131470451559748,0.005129129271544126,pixel_mse,0.00015960568907131406,30,0.004101960813450619,0.030228655747281733,0.018740534296128174
+int4_convrot,0.10966270520278175,0.035462206958996906,0.053908361044356125,0.0221988674500555,0.023956278593986097,0.04868999446064779,0.008990714826063293,pixel_mse,0.0005065721451857885,30,0.009463978488066623,0.05558737746307039,0.03276640409085106
+gguf_q8_0,0.04224698943996122,0.006084113056624834,0.012546890309027867,0.0032126671962391354,0.0032267241388587733,0.00535753783029843,0.00018873858208390105,pixel_mse,1.0346624360637463e-05,30,0.00016711356790888226,0.006232682232722279,0.009222189905145867
+gguf_q4_k_m,0.05410322656455432,0.015988204455365276,0.02298823886850115,0.010055979277549197,0.013484952925808629,0.02159108579938171,0.0027473493818909603,pixel_mse,8.974452157370014e-05,30,0.002742936379368872,0.02360484531762232,0.014507896405886843
 bf16,0.0,0.0,0.0,0.0,0.0,0.0,0.0,pixel_rmse,0.0,30,0.0,0.0,0.0
+fp8_scaled,0.21566447771270142,0.08551863220535331,0.11803884160886993,0.059246730721562946,0.08585596062258541,0.10460753085335903,0.029723176883719483,pixel_rmse,0.006776241776919497,30,0.029187427565051216,0.11802769274312241,0.06069099289716499
+int8_convrot,0.19602162551072594,0.0605931022400491,0.08699692835372667,0.042165733712456695,0.045686307281696814,0.07669131020398608,0.01937317013472623,pixel_rmse,0.004587142858076008,30,0.01713523582174047,0.08474941534403135,0.05033047941957317
+mxfp8,0.21474084348667896,0.07425315291442554,0.10409143136110337,0.051802989341558955,0.07125237252473124,0.08511686264605975,0.024019798834631994,pixel_rmse,0.008681711610280942,30,0.023115811369805367,0.10109378242848045,0.05593504174339372
+nvfp4,0.2906585351920953,0.12526690155312523,0.1526859822661448,0.0952640210061375,0.129479922655383,0.1487664964014403,0.07308798585884464,pixel_rmse,0.01263351451779409,30,0.06365347923449301,0.17380182099892122,0.06727044699127928
+int4_convrot,0.3311535975990322,0.16701950298968368,0.21192818209130662,0.12649684752687093,0.15477683332471937,0.22065809402930994,0.09481938001307166,pixel_rmse,0.022507157643420646,30,0.09719586151229953,0.23561163247815953,0.08847379845960979
+gguf_q8_0,0.2055407245291337,0.05989122814657812,0.0876641776819657,0.041248686695683635,0.05667796446803827,0.07304838730542187,0.013738216117236657,pixel_rmse,0.00321661691232224,30,0.012918577004340922,0.07894397648612052,0.05082580644799446
+gguf_q4_k_m,0.23260100293110156,0.11093333250893217,0.1390401667028396,0.081085591426681,0.11609699090666219,0.14678054077366665,0.052415096044658646,pixel_rmse,0.009473358516054384,30,0.05237299936970141,0.15363868300349118,0.06171681875917674
 bf16,1.0,1.0,1.0,1.0,1.0,1.0,1.0,ssim_luminance,1.0,30,1.0,1.0,0.0
+fp8_scaled,0.9947213409839686,0.8495422321157533,0.9062598247238408,0.7716002865871747,0.8714935187035986,0.9598562235679247,0.8041937490377443,ssim_luminance,0.5374379639887356,30,0.8035385577103054,0.9663904945389548,0.13608721815065564
+int8_convrot,0.9972517586996246,0.9111058857313673,0.945425006417867,0.8511607674730113,0.9482424712836082,0.9778833355656921,0.876726818935421,ssim_luminance,0.5770211169880319,30,0.8709708878893548,0.986583532170725,0.09798492932033326
+mxfp8,0.9945743645380596,0.8730140327040307,0.9207223625572418,0.7991857799330204,0.9013037337013577,0.9759980211249115,0.8475112634561334,ssim_luminance,0.5457123650284678,30,0.8394579106397722,0.9817830528659783,0.12381098549546848
+nvfp4,0.9906649380505849,0.7731607227537941,0.840461719461983,0.7063214650665696,0.7997759755317175,0.8746571129294994,0.6444890235190676,ssim_luminance,0.5126984077941714,30,0.6430643514080403,0.8805074212911161,0.149463063738976
+int4_convrot,0.9816022060130221,0.7019620353808745,0.7874713065226747,0.6209851362711211,0.6674100672053789,0.8444254285628936,0.5637628799606004,ssim_luminance,0.429950505228912,30,0.5589832905284916,0.8573397476319746,0.17065306085374024
+gguf_q8_0,0.9977254449120735,0.9104189379877191,0.9470661638497989,0.8441187251164576,0.9441790416683957,0.9868257556785974,0.879068387855165,ssim_luminance,0.5582122449800988,30,0.8706624657582591,0.9890242876553007,0.10210790490582666
+gguf_q4_k_m,0.9932246167317273,0.8006335780079745,0.8670279274239481,0.7296118463942242,0.8265484820162927,0.9211802067999066,0.7147146499760747,ssim_luminance,0.5281362865692519,30,0.6725067515434904,0.930582858610245,0.14397464848722236
 bf16,1.0,1.0,1.0,1.0,1.0,1.0,1.0,ssim_rgb,1.0,30,1.0,1.0,0.0
+fp8_scaled,0.9919694066047668,0.8482623716195424,0.9043818093206002,0.7683190470289246,0.8743238747119904,0.9592909216880798,0.7988818287849426,ssim_rgb,0.5347716212272644,30,0.7983435392379761,0.9660519659519196,0.1373303919003205
+int8_convrot,0.9962493777275085,0.9105293810367584,0.9449910359654691,0.8521181271628765,0.9480270147323608,0.9772445559501648,0.8792413771152496,ssim_rgb,0.5750747323036194,30,0.8703338503837585,0.9863479137420654,0.09835773558757431
+mxfp8,0.9942013621330261,0.8716254810492198,0.9199688293958901,0.7978358702691973,0.9016529023647308,0.9762930870056152,0.8465492129325867,ssim_rgb,0.5429816842079163,30,0.8388548493385315,0.980877161026001,0.12433808969369654
+nvfp4,0.985677182674408,0.770771857102712,0.8368322263305347,0.7041574133808767,0.7963234782218933,0.8719966411590576,0.6420286297798157,ssim_rgb,0.5119650363922119,30,0.6388882994651794,0.8811187148094177,0.14975735526647088
+int4_convrot,0.9573716521263123,0.6957410534222921,0.779200343023506,0.6140394005768359,0.6623322665691376,0.8305498957633972,0.555009126663208,ssim_rgb,0.4275805950164795,30,0.5456289052963257,0.8421603292226791,0.16863765113291782
+gguf_q8_0,0.997218132019043,0.9098504861195882,0.9464978386195159,0.8439580584385631,0.9440278708934784,0.9865734875202179,0.8837575912475586,ssim_rgb,0.5561327338218689,30,0.8704406023025513,0.9891497045755386,0.1026313186430599
+gguf_q4_k_m,0.9891679883003235,0.7988339940706889,0.8645451080713356,0.7273717896400881,0.8255289196968079,0.9204159080982208,0.7132329940795898,ssim_rgb,0.5271547436714172,30,0.6718244701623917,0.9301868230104446,0.14483328455984115

metrics/latency_components.csv CHANGED Viewed

@@ -4,148 +4,238 @@ fp8_scaled,p01_portrait,0,p01_portrait__r0__fp8_scaled,19.518150000003516,2.4397
 int8_convrot,p01_portrait,0,p01_portrait__r0__int8_convrot,12.41274020000128,1.55159252500016,0.34310439999535447
 mxfp8,p01_portrait,0,p01_portrait__r0__mxfp8,31.934214000000793,3.991776750000099,0.3437412999992375
 nvfp4,p01_portrait,0,p01_portrait__r0__nvfp4,24.997657799998706,3.124707224999838,0.3422749000019394
 bf16,p01_portrait,1,p01_portrait__r1__bf16,25.766126800001075,3.2207658500001344,0.36054710000462364
 fp8_scaled,p01_portrait,1,p01_portrait__r1__fp8_scaled,19.507955900000525,2.4384944875000656,0.3419565000003786
 int8_convrot,p01_portrait,1,p01_portrait__r1__int8_convrot,12.415675199998077,1.5519593999997596,0.34560680000140565
 mxfp8,p01_portrait,1,p01_portrait__r1__mxfp8,31.931189099996118,3.9913986374995147,0.3470567000040319
 nvfp4,p01_portrait,1,p01_portrait__r1__nvfp4,24.91491429999587,3.114364287499484,0.3429709999982151
 bf16,p02_hands_group,0,p02_hands_group__r0__bf16,26.55899060000229,3.319873825000286,0.3593129000000772
 fp8_scaled,p02_hands_group,0,p02_hands_group__r0__fp8_scaled,19.829203200002667,2.4786504000003333,0.3649491999967722
 int8_convrot,p02_hands_group,0,p02_hands_group__r0__int8_convrot,12.800082400004612,1.6000103000005765,0.37219619999814313
 mxfp8,p02_hands_group,0,p02_hands_group__r0__mxfp8,32.05036200000177,4.006295250000221,0.343833300001279
 nvfp4,p02_hands_group,0,p02_hands_group__r0__nvfp4,25.075064699994982,3.1343830874993728,0.36645429999771295
 bf16,p02_hands_group,1,p02_hands_group__r1__bf16,25.73294419999729,3.2166180249996614,0.35790699999779463
 fp8_scaled,p02_hands_group,1,p02_hands_group__r1__fp8_scaled,19.440449700006866,2.4300562125008582,0.3421442999970168
 int8_convrot,p02_hands_group,1,p02_hands_group__r1__int8_convrot,12.393452299998899,1.5491815374998623,0.34263020000071265
 mxfp8,p02_hands_group,1,p02_hands_group__r1__mxfp8,31.90537680000125,3.988172100000156,0.3467912000051001
 nvfp4,p02_hands_group,1,p02_hands_group__r1__nvfp4,24.88655359999393,3.1108191999992414,0.34178569999494357
 bf16,p03_package_text,0,p03_package_text__r0__bf16,26.428096400006325,3.3035120500007906,0.35971660000359407
 fp8_scaled,p03_package_text,0,p03_package_text__r0__fp8_scaled,19.86523429999943,2.4831542874999286,0.364295300001686
 int8_convrot,p03_package_text,0,p03_package_text__r0__int8_convrot,12.833145600001444,1.6041432000001805,0.36185370000021067
 mxfp8,p03_package_text,0,p03_package_text__r0__mxfp8,32.122544700003345,4.015318087500418,0.34582419999787817
 nvfp4,p03_package_text,0,p03_package_text__r0__nvfp4,25.11859700000059,3.139824625000074,0.36579490000440273
 bf16,p03_package_text,1,p03_package_text__r1__bf16,25.754473699998925,3.2193092124998657,0.3597641000014846
 fp8_scaled,p03_package_text,1,p03_package_text__r1__fp8_scaled,19.477579200000037,2.4346974000000046,0.3415906000009272
 int8_convrot,p03_package_text,1,p03_package_text__r1__int8_convrot,12.415656699995452,1.5519570874994315,0.3418060999974841
 mxfp8,p03_package_text,1,p03_package_text__r1__mxfp8,31.926340099998924,3.9907925124998656,0.3444383000023663
 nvfp4,p03_package_text,1,p03_package_text__r1__nvfp4,24.92009930000495,3.1150124125006187,0.34373210000194376
 bf16,p04_storefront_text,0,p04_storefront_text__r0__bf16,26.43898760000593,3.3048734500007413,0.3620234999980312
 fp8_scaled,p04_storefront_text,0,p04_storefront_text__r0__fp8_scaled,19.808458699997573,2.4760573374996966,0.3645692999998573
 int8_convrot,p04_storefront_text,0,p04_storefront_text__r0__int8_convrot,12.824930000002496,1.603116250000312,0.3721417999986443
 mxfp8,p04_storefront_text,0,p04_storefront_text__r0__mxfp8,32.046656300000905,4.005832037500113,0.34455769999476615
 nvfp4,p04_storefront_text,0,p04_storefront_text__r0__nvfp4,25.166546899999958,3.1458183624999947,0.36426750000100583
 bf16,p04_storefront_text,1,p04_storefront_text__r1__bf16,25.749647199998435,3.2187058999998044,0.3618012999941129
 fp8_scaled,p04_storefront_text,1,p04_storefront_text__r1__fp8_scaled,19.497219400000176,2.437152425000022,0.34386569999333005
 int8_convrot,p04_storefront_text,1,p04_storefront_text__r1__int8_convrot,12.419003399998473,1.5523754249998092,0.3552319999944302
 mxfp8,p04_storefront_text,1,p04_storefront_text__r1__mxfp8,31.927101900000707,3.9908877375000884,0.34623740000097314
 nvfp4,p04_storefront_text,1,p04_storefront_text__r1__nvfp4,24.982380599998578,3.1227975749998222,0.3431907999984105
 bf16,p05_architecture,0,p05_architecture__r0__bf16,26.42644499999733,3.303305624999666,0.36065249999955995
 fp8_scaled,p05_architecture,0,p05_architecture__r0__fp8_scaled,19.84195870000258,2.4802448375003223,0.361715599996387
 int8_convrot,p05_architecture,0,p05_architecture__r0__int8_convrot,12.811952299998666,1.6014940374998332,0.3728253000008408
 mxfp8,p05_architecture,0,p05_architecture__r0__mxfp8,32.058604599995306,4.007325574999413,0.3451121999969473
 nvfp4,p05_architecture,0,p05_architecture__r0__nvfp4,25.076200399998925,3.1345250499998656,0.39036050000140676
 bf16,p05_architecture,1,p05_architecture__r1__bf16,25.747276499998407,3.218409562499801,0.3594087000019499
 fp8_scaled,p05_architecture,1,p05_architecture__r1__fp8_scaled,19.482401900000696,2.435300237500087,0.3413265999988653
 int8_convrot,p05_architecture,1,p05_architecture__r1__int8_convrot,12.398516900000686,1.5498146125000858,0.34373239999695215
 mxfp8,p05_architecture,1,p05_architecture__r1__mxfp8,31.906311900005676,3.9882889875007095,0.346149000004516
 nvfp4,p05_architecture,1,p05_architecture__r1__nvfp4,24.887610999998287,3.110951374999786,0.3414497999983723
 bf16,p06_reflections,0,p06_reflections__r0__bf16,26.413289600001008,3.301661200000126,0.36045620000368217
 fp8_scaled,p06_reflections,0,p06_reflections__r0__fp8_scaled,19.807194799999706,2.4758993499999633,0.3622964000023785
 int8_convrot,p06_reflections,0,p06_reflections__r0__int8_convrot,12.805317400001513,1.6006646750001892,0.36017700000229524
 mxfp8,p06_reflections,0,p06_reflections__r0__mxfp8,32.03812069999549,4.004765087499436,0.3443392999979551
 nvfp4,p06_reflections,0,p06_reflections__r0__nvfp4,25.104682299999695,3.138085287499962,0.3658103000052506
 bf16,p06_reflections,1,p06_reflections__r1__bf16,25.7696350000042,3.221204375000525,0.3624214999945252
 fp8_scaled,p06_reflections,1,p06_reflections__r1__fp8_scaled,19.45394440000382,2.4317430500004775,0.34223780000320403
 int8_convrot,p06_reflections,1,p06_reflections__r1__int8_convrot,12.377740899995842,1.5472176124994803,0.34203720000368776
 mxfp8,p06_reflections,1,p06_reflections__r1__mxfp8,31.907638199998473,3.988454774999809,0.34380390000296757
 nvfp4,p06_reflections,1,p06_reflections__r1__nvfp4,24.88203130000329,3.1102539125004114,0.34354329999769107
 bf16,p07_foliage_bird,0,p07_foliage_bird__r0__bf16,26.3315333999999,3.2914416749999873,0.36088089999975637
 fp8_scaled,p07_foliage_bird,0,p07_foliage_bird__r0__fp8_scaled,19.66935349999403,2.458669187499254,0.36557429999811575
 int8_convrot,p07_foliage_bird,0,p07_foliage_bird__r0__int8_convrot,12.718108300003223,1.589763537500403,0.3733365000007325
 mxfp8,p07_foliage_bird,0,p07_foliage_bird__r0__mxfp8,32.311203999997815,4.038900499999727,0.3433459000007133
 nvfp4,p07_foliage_bird,0,p07_foliage_bird__r0__nvfp4,24.98653000000195,3.1233162500002436,0.3638533000048483
 bf16,p07_foliage_bird,1,p07_foliage_bird__r1__bf16,25.66702929999883,3.208378662499854,0.3615896000046632
 fp8_scaled,p07_foliage_bird,1,p07_foliage_bird__r1__fp8_scaled,19.346259199999622,2.4182823999999528,0.34041009999782545
 int8_convrot,p07_foliage_bird,1,p07_foliage_bird__r1__int8_convrot,12.30146319999767,1.5376828999997088,0.35326789999817265
 mxfp8,p07_foliage_bird,1,p07_foliage_bird__r1__mxfp8,31.822317199999816,3.977789649999977,0.3452991000012844
 nvfp4,p07_foliage_bird,1,p07_foliage_bird__r1__nvfp4,24.88409369999863,3.1105117124998287,0.34237080000457354
 bf16,p08_fog_neon,0,p08_fog_neon__r0__bf16,26.342836100004206,3.2928545125005257,0.3599956999969436
 fp8_scaled,p08_fog_neon,0,p08_fog_neon__r0__fp8_scaled,19.73903650000284,2.467379562500355,0.36486839999997756
 int8_convrot,p08_fog_neon,0,p08_fog_neon__r0__int8_convrot,12.660384799994063,1.5825480999992578,0.3578401999984635
 mxfp8,p08_fog_neon,0,p08_fog_neon__r0__mxfp8,32.0215584999969,4.0026948124996125,0.3458612999966135
 nvfp4,p08_fog_neon,0,p08_fog_neon__r0__nvfp4,24.983396399999037,3.1229245499998797,0.3660890999963158
 bf16,p08_fog_neon,1,p08_fog_neon__r1__bf16,25.73160349999671,3.2164504374995886,0.3584536999987904
 fp8_scaled,p08_fog_neon,1,p08_fog_neon__r1__fp8_scaled,19.452148899996246,2.4315186124995307,0.34226489999855403
 int8_convrot,p08_fog_neon,1,p08_fog_neon__r1__int8_convrot,12.371107699997083,1.5463884624996354,0.34410080000088783
 mxfp8,p08_fog_neon,1,p08_fog_neon__r1__mxfp8,31.901877699994657,3.987734712499332,0.3437627000021166
 nvfp4,p08_fog_neon,1,p08_fog_neon__r1__nvfp4,24.880305899998348,3.1100382374997935,0.3463271000000532
 bf16,p09_material_macro,0,p09_material_macro__r0__bf16,26.355002300006163,3.2943752875007704,0.36106889999791747
 fp8_scaled,p09_material_macro,0,p09_material_macro__r0__fp8_scaled,19.70316059999459,2.4628950749993237,0.36208410000108415
 int8_convrot,p09_material_macro,0,p09_material_macro__r0__int8_convrot,12.728351700003259,1.5910439625004074,0.3715217999997549
 mxfp8,p09_material_macro,0,p09_material_macro__r0__mxfp8,32.009764800000994,4.001220600000124,0.34393160000036005
 nvfp4,p09_material_macro,0,p09_material_macro__r0__nvfp4,25.076785599994764,3.1345981999993455,0.3656614999999874
 bf16,p09_material_macro,1,p09_material_macro__r1__bf16,25.69524629999796,3.211905787499745,0.35989620000327704
 fp8_scaled,p09_material_macro,1,p09_material_macro__r1__fp8_scaled,19.360541899994132,2.4200677374992665,0.3411396000010427
 int8_convrot,p09_material_macro,1,p09_material_macro__r1__int8_convrot,12.321569399995496,1.540196174999437,0.34455739999975776
 mxfp8,p09_material_macro,1,p09_material_macro__r1__mxfp8,31.863198700004432,3.982899837500554,0.3462238999927649
 nvfp4,p09_material_macro,1,p09_material_macro__r1__nvfp4,24.835008399997605,3.1043760499997006,0.34106790000078036
 bf16,p10_vector_gradient,0,p10_vector_gradient__r0__bf16,26.41063660000509,3.3013295750006364,0.3622260999982245
 fp8_scaled,p10_vector_gradient,0,p10_vector_gradient__r0__fp8_scaled,19.791433999998844,2.4739292499998555,0.3632878999997047
 int8_convrot,p10_vector_gradient,0,p10_vector_gradient__r0__int8_convrot,12.803664499995648,1.600458062499456,0.37378200000239303
 mxfp8,p10_vector_gradient,0,p10_vector_gradient__r0__mxfp8,32.07423589999962,4.009279487499953,0.3469769000002998
 nvfp4,p10_vector_gradient,0,p10_vector_gradient__r0__nvfp4,25.130464199995913,3.141308024999489,0.3634611000015866
 bf16,p10_vector_gradient,1,p10_vector_gradient__r1__bf16,25.740654799999902,3.2175818499999878,0.3583937000003061
 fp8_scaled,p10_vector_gradient,1,p10_vector_gradient__r1__fp8_scaled,19.49589990000095,2.4369874875001187,0.340949700002966
 int8_convrot,p10_vector_gradient,1,p10_vector_gradient__r1__int8_convrot,12.38974589999998,1.5487182374999975,0.35242319999815663
 mxfp8,p10_vector_gradient,1,p10_vector_gradient__r1__mxfp8,31.892903699997987,3.9866129624997484,0.34392099999968195
 nvfp4,p10_vector_gradient,1,p10_vector_gradient__r1__nvfp4,24.889698499995575,3.111212312499447,0.34209610000107205
 bf16,p11_retro_anime,0,p11_retro_anime__r0__bf16,26.427372999998624,3.303421624999828,0.36001230000692885
 fp8_scaled,p11_retro_anime,0,p11_retro_anime__r0__fp8_scaled,19.82015749999846,2.4775196874998073,0.3625553999954718
 int8_convrot,p11_retro_anime,0,p11_retro_anime__r0__int8_convrot,12.793321099998138,1.5991651374997673,0.37392710000131046
 mxfp8,p11_retro_anime,0,p11_retro_anime__r0__mxfp8,32.069333699997514,4.008666712499689,0.34537339999951655
 nvfp4,p11_retro_anime,0,p11_retro_anime__r0__nvfp4,25.071917300003406,3.1339896625004258,0.36412510000081966
 bf16,p11_retro_anime,1,p11_retro_anime__r1__bf16,25.738796300000104,3.217349537500013,0.3581162999980734
 fp8_scaled,p11_retro_anime,1,p11_retro_anime__r1__fp8_scaled,19.491088599999784,2.436386074999973,0.3423934000020381
 int8_convrot,p11_retro_anime,1,p11_retro_anime__r1__int8_convrot,12.400373699994816,1.550046712499352,0.3561979000005522
 mxfp8,p11_retro_anime,1,p11_retro_anime__r1__mxfp8,31.915469100000337,3.989433637500042,0.34709660000225995
 nvfp4,p11_retro_anime,1,p11_retro_anime__r1__nvfp4,24.91180919999897,3.1139761499998713,0.3412951999998768
 bf16,p12_watercolor,0,p12_watercolor__r0__bf16,26.340583699995477,3.2925729624994347,0.3605783999955747
 fp8_scaled,p12_watercolor,0,p12_watercolor__r0__fp8_scaled,19.71092110000609,2.463865137500761,0.36248039999918547
 int8_convrot,p12_watercolor,0,p12_watercolor__r0__int8_convrot,12.736942100003944,1.592117762500493,0.37184930000512395
 mxfp8,p12_watercolor,0,p12_watercolor__r0__mxfp8,31.99003079999966,3.9987538499999573,0.3437904000020353
 nvfp4,p12_watercolor,0,p12_watercolor__r0__nvfp4,25.040403200000583,3.130050400000073,0.3656928000054904
 bf16,p12_watercolor,1,p12_watercolor__r1__bf16,25.708062799996696,3.213507849999587,0.35866660000465345
 fp8_scaled,p12_watercolor,1,p12_watercolor__r1__fp8_scaled,19.33984490000148,2.417480612500185,0.34357749999617226
 int8_convrot,p12_watercolor,1,p12_watercolor__r1__int8_convrot,12.33782169999904,1.54222771249988,0.3531944999995176
 mxfp8,p12_watercolor,1,p12_watercolor__r1__mxfp8,31.847633999997925,3.9809542499997406,0.34513710000464926
 nvfp4,p12_watercolor,1,p12_watercolor__r1__nvfp4,24.82736699999805,3.103420874999756,0.3418500999978278
 bf16,p13_food,0,p13_food__r0__bf16,26.28882750000048,3.28610343750006,0.359178200000315
 fp8_scaled,p13_food,0,p13_food__r0__fp8_scaled,19.57829430000129,2.447286787500161,0.36358910000126343
 int8_convrot,p13_food,0,p13_food__r0__int8_convrot,12.59504530000413,1.5743806625005163,0.3585473999992246
 mxfp8,p13_food,0,p13_food__r0__mxfp8,31.915211999999883,3.9894014999999854,0.3469457999963197
 nvfp4,p13_food,0,p13_food__r0__nvfp4,24.89938080000138,3.1124226000001727,0.36475969999446534
 bf16,p13_food,1,p13_food__r1__bf16,25.687490700001945,3.210936337500243,0.3574178000053507
 fp8_scaled,p13_food,1,p13_food__r1__fp8_scaled,19.317986800000654,2.4147483500000817,0.3451076999990619
 int8_convrot,p13_food,1,p13_food__r1__int8_convrot,12.318170099999406,1.5397712624999258,0.3425465999971493
 mxfp8,p13_food,1,p13_food__r1__mxfp8,31.82090210000024,3.97761276250003,0.3468256000051042
 nvfp4,p13_food,1,p13_food__r1__nvfp4,24.812316700001247,3.101539587500156,0.3419875999970827
 bf16,p14_spatial_counts,0,p14_spatial_counts__r0__bf16,26.303392399997392,3.287924049999674,0.3585942000063369
 fp8_scaled,p14_spatial_counts,0,p14_spatial_counts__r0__fp8_scaled,19.04544380000152,2.38068047500019,0.33408699999563396
 int8_convrot,p14_spatial_counts,0,p14_spatial_counts__r0__int8_convrot,12.620540500000061,1.5775675625000076,0.3720668999958434
 mxfp8,p14_spatial_counts,0,p14_spatial_counts__r0__mxfp8,31.957430399997975,3.994678799999747,0.3476455999989412
 nvfp4,p14_spatial_counts,0,p14_spatial_counts__r0__nvfp4,24.93046460000187,3.1163080750002337,0.3636112000021967
 bf16,p14_spatial_counts,1,p14_spatial_counts__r1__bf16,25.75583330000518,3.2194791625006474,0.3613586999999825
 fp8_scaled,p14_spatial_counts,1,p14_spatial_counts__r1__fp8_scaled,19.16530440000497,2.3956630500006213,0.34405169999809004
 int8_convrot,p14_spatial_counts,1,p14_spatial_counts__r1__int8_convrot,12.32397349999519,1.5404966874993988,0.3424712999985786
 mxfp8,p14_spatial_counts,1,p14_spatial_counts__r1__mxfp8,31.860219800000777,3.982527475000097,0.34347580000030575
 nvfp4,p14_spatial_counts,1,p14_spatial_counts__r1__nvfp4,24.90657089999877,3.113321362499846,0.3416189999989001
 bf16,p15_scientific_poster,0,p15_scientific_poster__r0__bf16,26.389044000003196,3.2986305000003995,0.3583375000016531
 fp8_scaled,p15_scientific_poster,0,p15_scientific_poster__r0__fp8_scaled,19.766978100000415,2.470872262500052,0.36253020000003744
 int8_convrot,p15_scientific_poster,0,p15_scientific_poster__r0__int8_convrot,12.658317000001261,1.5822896250001577,0.3722353000048315
 mxfp8,p15_scientific_poster,0,p15_scientific_poster__r0__mxfp8,32.03057660000195,4.003822075000244,0.343433100002585
 nvfp4,p15_scientific_poster,0,p15_scientific_poster__r0__nvfp4,24.988446399998793,3.123555799999849,0.3658660000000964
 bf16,p15_scientific_poster,1,p15_scientific_poster__r1__bf16,25.796780199998466,3.2245975249998082,0.3632294999988517
 fp8_scaled,p15_scientific_poster,1,p15_scientific_poster__r1__fp8_scaled,19.49097730000358,2.4363721625004473,0.3413591999997152
 int8_convrot,p15_scientific_poster,1,p15_scientific_poster__r1__int8_convrot,12.39311440000165,1.5491393000002063,0.3534289000017452
 mxfp8,p15_scientific_poster,1,p15_scientific_poster__r1__mxfp8,31.907068100001197,3.9883835125001497,0.3435710999983712
 nvfp4,p15_scientific_poster,1,p15_scientific_poster__r1__nvfp4,24.887707000001683,3.1109633750002104,0.34383209999941755

 int8_convrot,p01_portrait,0,p01_portrait__r0__int8_convrot,12.41274020000128,1.55159252500016,0.34310439999535447
 mxfp8,p01_portrait,0,p01_portrait__r0__mxfp8,31.934214000000793,3.991776750000099,0.3437412999992375
 nvfp4,p01_portrait,0,p01_portrait__r0__nvfp4,24.997657799998706,3.124707224999838,0.3422749000019394
+int4_convrot,p01_portrait,0,p01_portrait__r0__int4_convrot,9.540271999998367,1.192533999999796,0.35004749999643536
+gguf_q8_0,p01_portrait,0,p01_portrait__r0__gguf_q8_0,27.486214800002926,3.435776850000366,0.8310684999960358
+gguf_q4_k_m,p01_portrait,0,p01_portrait__r0__gguf_q4_k_m,31.345965000000433,3.918245625000054,0.3419159999975818
 bf16,p01_portrait,1,p01_portrait__r1__bf16,25.766126800001075,3.2207658500001344,0.36054710000462364
 fp8_scaled,p01_portrait,1,p01_portrait__r1__fp8_scaled,19.507955900000525,2.4384944875000656,0.3419565000003786
 int8_convrot,p01_portrait,1,p01_portrait__r1__int8_convrot,12.415675199998077,1.5519593999997596,0.34560680000140565
 mxfp8,p01_portrait,1,p01_portrait__r1__mxfp8,31.931189099996118,3.9913986374995147,0.3470567000040319
 nvfp4,p01_portrait,1,p01_portrait__r1__nvfp4,24.91491429999587,3.114364287499484,0.3429709999982151
+int4_convrot,p01_portrait,1,p01_portrait__r1__int4_convrot,9.558170700001938,1.1947713375002422,0.3441594000032637
+gguf_q8_0,p01_portrait,1,p01_portrait__r1__gguf_q8_0,27.500255200000538,3.4375319000000673,0.808678699999291
+gguf_q4_k_m,p01_portrait,1,p01_portrait__r1__gguf_q4_k_m,31.383951399999205,3.9229939249999006,0.3405791999975918
 bf16,p02_hands_group,0,p02_hands_group__r0__bf16,26.55899060000229,3.319873825000286,0.3593129000000772
 fp8_scaled,p02_hands_group,0,p02_hands_group__r0__fp8_scaled,19.829203200002667,2.4786504000003333,0.3649491999967722
 int8_convrot,p02_hands_group,0,p02_hands_group__r0__int8_convrot,12.800082400004612,1.6000103000005765,0.37219619999814313
 mxfp8,p02_hands_group,0,p02_hands_group__r0__mxfp8,32.05036200000177,4.006295250000221,0.343833300001279
 nvfp4,p02_hands_group,0,p02_hands_group__r0__nvfp4,25.075064699994982,3.1343830874993728,0.36645429999771295
+int4_convrot,p02_hands_group,0,p02_hands_group__r0__int4_convrot,9.970703500002855,1.246337937500357,0.36170720000518486
+gguf_q8_0,p02_hands_group,0,p02_hands_group__r0__gguf_q8_0,28.543118599998706,3.5678898249998383,0.774508299997251
+gguf_q4_k_m,p02_hands_group,0,p02_hands_group__r0__gguf_q4_k_m,31.889467799999693,3.9861834749999616,0.3573049000042374
 bf16,p02_hands_group,1,p02_hands_group__r1__bf16,25.73294419999729,3.2166180249996614,0.35790699999779463
 fp8_scaled,p02_hands_group,1,p02_hands_group__r1__fp8_scaled,19.440449700006866,2.4300562125008582,0.3421442999970168
 int8_convrot,p02_hands_group,1,p02_hands_group__r1__int8_convrot,12.393452299998899,1.5491815374998623,0.34263020000071265
 mxfp8,p02_hands_group,1,p02_hands_group__r1__mxfp8,31.90537680000125,3.988172100000156,0.3467912000051001
 nvfp4,p02_hands_group,1,p02_hands_group__r1__nvfp4,24.88655359999393,3.1108191999992414,0.34178569999494357
+int4_convrot,p02_hands_group,1,p02_hands_group__r1__int4_convrot,9.513311799993971,1.1891639749992464,0.3426818000007188
+gguf_q8_0,p02_hands_group,1,p02_hands_group__r1__gguf_q8_0,27.440405000001192,3.430050625000149,0.748263900000893
+gguf_q4_k_m,p02_hands_group,1,p02_hands_group__r1__gguf_q4_k_m,31.32009249999828,3.915011562499785,0.34318550000170944
 bf16,p03_package_text,0,p03_package_text__r0__bf16,26.428096400006325,3.3035120500007906,0.35971660000359407
 fp8_scaled,p03_package_text,0,p03_package_text__r0__fp8_scaled,19.86523429999943,2.4831542874999286,0.364295300001686
 int8_convrot,p03_package_text,0,p03_package_text__r0__int8_convrot,12.833145600001444,1.6041432000001805,0.36185370000021067
 mxfp8,p03_package_text,0,p03_package_text__r0__mxfp8,32.122544700003345,4.015318087500418,0.34582419999787817
 nvfp4,p03_package_text,0,p03_package_text__r0__nvfp4,25.11859700000059,3.139824625000074,0.36579490000440273
+int4_convrot,p03_package_text,0,p03_package_text__r0__int4_convrot,9.98690959999658,1.2483636999995724,0.3623985000012908
+gguf_q8_0,p03_package_text,0,p03_package_text__r0__gguf_q8_0,28.459787399995548,3.5574734249994435,0.77522759999556
+gguf_q4_k_m,p03_package_text,0,p03_package_text__r0__gguf_q4_k_m,31.88404879999871,3.9855060999998386,0.36111369999707676
 bf16,p03_package_text,1,p03_package_text__r1__bf16,25.754473699998925,3.2193092124998657,0.3597641000014846
 fp8_scaled,p03_package_text,1,p03_package_text__r1__fp8_scaled,19.477579200000037,2.4346974000000046,0.3415906000009272
 int8_convrot,p03_package_text,1,p03_package_text__r1__int8_convrot,12.415656699995452,1.5519570874994315,0.3418060999974841
 mxfp8,p03_package_text,1,p03_package_text__r1__mxfp8,31.926340099998924,3.9907925124998656,0.3444383000023663
 nvfp4,p03_package_text,1,p03_package_text__r1__nvfp4,24.92009930000495,3.1150124125006187,0.34373210000194376
+int4_convrot,p03_package_text,1,p03_package_text__r1__int4_convrot,9.564303999999538,1.1955379999999423,0.3426225000002887
+gguf_q8_0,p03_package_text,1,p03_package_text__r1__gguf_q8_0,27.469022599994787,3.4336278249993484,0.747886599994672
+gguf_q4_k_m,p03_package_text,1,p03_package_text__r1__gguf_q4_k_m,31.395732999997563,3.9244666249996953,0.3406795000046259
 bf16,p04_storefront_text,0,p04_storefront_text__r0__bf16,26.43898760000593,3.3048734500007413,0.3620234999980312
 fp8_scaled,p04_storefront_text,0,p04_storefront_text__r0__fp8_scaled,19.808458699997573,2.4760573374996966,0.3645692999998573
 int8_convrot,p04_storefront_text,0,p04_storefront_text__r0__int8_convrot,12.824930000002496,1.603116250000312,0.3721417999986443
 mxfp8,p04_storefront_text,0,p04_storefront_text__r0__mxfp8,32.046656300000905,4.005832037500113,0.34455769999476615
 nvfp4,p04_storefront_text,0,p04_storefront_text__r0__nvfp4,25.166546899999958,3.1458183624999947,0.36426750000100583
+int4_convrot,p04_storefront_text,0,p04_storefront_text__r0__int4_convrot,9.988875500006543,1.2486094375008179,0.3644720000011148
+gguf_q8_0,p04_storefront_text,0,p04_storefront_text__r0__gguf_q8_0,28.462883399995917,3.5578604249994896,0.7737066000045161
+gguf_q4_k_m,p04_storefront_text,0,p04_storefront_text__r0__gguf_q4_k_m,31.930641200000537,3.991330150000067,0.35846200000378303
 bf16,p04_storefront_text,1,p04_storefront_text__r1__bf16,25.749647199998435,3.2187058999998044,0.3618012999941129
 fp8_scaled,p04_storefront_text,1,p04_storefront_text__r1__fp8_scaled,19.497219400000176,2.437152425000022,0.34386569999333005
 int8_convrot,p04_storefront_text,1,p04_storefront_text__r1__int8_convrot,12.419003399998473,1.5523754249998092,0.3552319999944302
 mxfp8,p04_storefront_text,1,p04_storefront_text__r1__mxfp8,31.927101900000707,3.9908877375000884,0.34623740000097314
 nvfp4,p04_storefront_text,1,p04_storefront_text__r1__nvfp4,24.982380599998578,3.1227975749998222,0.3431907999984105
+int4_convrot,p04_storefront_text,1,p04_storefront_text__r1__int4_convrot,9.53329500000109,1.1916618750001362,0.34437590000015916
+gguf_q8_0,p04_storefront_text,1,p04_storefront_text__r1__gguf_q8_0,27.48239879999892,3.435299849999865,0.7503400999994483
+gguf_q4_k_m,p04_storefront_text,1,p04_storefront_text__r1__gguf_q4_k_m,31.418690999998944,3.927336374999868,0.3425829999978305
 bf16,p05_architecture,0,p05_architecture__r0__bf16,26.42644499999733,3.303305624999666,0.36065249999955995
 fp8_scaled,p05_architecture,0,p05_architecture__r0__fp8_scaled,19.84195870000258,2.4802448375003223,0.361715599996387
 int8_convrot,p05_architecture,0,p05_architecture__r0__int8_convrot,12.811952299998666,1.6014940374998332,0.3728253000008408
 mxfp8,p05_architecture,0,p05_architecture__r0__mxfp8,32.058604599995306,4.007325574999413,0.3451121999969473
 nvfp4,p05_architecture,0,p05_architecture__r0__nvfp4,25.076200399998925,3.1345250499998656,0.39036050000140676
+int4_convrot,p05_architecture,0,p05_architecture__r0__int4_convrot,9.984325500001432,1.248040687500179,0.36482599999726517
+gguf_q8_0,p05_architecture,0,p05_architecture__r0__gguf_q8_0,28.434705899999244,3.5543382374999055,0.8038803000017651
+gguf_q4_k_m,p05_architecture,0,p05_architecture__r0__gguf_q4_k_m,31.819020300004922,3.9773775375006153,0.3584393999990425
 bf16,p05_architecture,1,p05_architecture__r1__bf16,25.747276499998407,3.218409562499801,0.3594087000019499
 fp8_scaled,p05_architecture,1,p05_architecture__r1__fp8_scaled,19.482401900000696,2.435300237500087,0.3413265999988653
 int8_convrot,p05_architecture,1,p05_architecture__r1__int8_convrot,12.398516900000686,1.5498146125000858,0.34373239999695215
 mxfp8,p05_architecture,1,p05_architecture__r1__mxfp8,31.906311900005676,3.9882889875007095,0.346149000004516
 nvfp4,p05_architecture,1,p05_architecture__r1__nvfp4,24.887610999998287,3.110951374999786,0.3414497999983723
+int4_convrot,p05_architecture,1,p05_architecture__r1__int4_convrot,9.541054199995415,1.1926317749994269,0.3442944999987958
+gguf_q8_0,p05_architecture,1,p05_architecture__r1__gguf_q8_0,27.448105999996187,3.4310132499995234,0.8277547999969102
+gguf_q4_k_m,p05_architecture,1,p05_architecture__r1__gguf_q4_k_m,31.32703169999877,3.9158789624998462,0.34380550000059884
 bf16,p06_reflections,0,p06_reflections__r0__bf16,26.413289600001008,3.301661200000126,0.36045620000368217
 fp8_scaled,p06_reflections,0,p06_reflections__r0__fp8_scaled,19.807194799999706,2.4758993499999633,0.3622964000023785
 int8_convrot,p06_reflections,0,p06_reflections__r0__int8_convrot,12.805317400001513,1.6006646750001892,0.36017700000229524
 mxfp8,p06_reflections,0,p06_reflections__r0__mxfp8,32.03812069999549,4.004765087499436,0.3443392999979551
 nvfp4,p06_reflections,0,p06_reflections__r0__nvfp4,25.104682299999695,3.138085287499962,0.3658103000052506
+int4_convrot,p06_reflections,0,p06_reflections__r0__int4_convrot,9.989767999999458,1.2487209999999322,0.3629326000009314
+gguf_q8_0,p06_reflections,0,p06_reflections__r0__gguf_q8_0,28.404644800000824,3.550580600000103,0.7900146999963908
+gguf_q4_k_m,p06_reflections,0,p06_reflections__r0__gguf_q4_k_m,31.835729799997353,3.979466224999669,0.35985070000606356
 bf16,p06_reflections,1,p06_reflections__r1__bf16,25.7696350000042,3.221204375000525,0.3624214999945252
 fp8_scaled,p06_reflections,1,p06_reflections__r1__fp8_scaled,19.45394440000382,2.4317430500004775,0.34223780000320403
 int8_convrot,p06_reflections,1,p06_reflections__r1__int8_convrot,12.377740899995842,1.5472176124994803,0.34203720000368776
 mxfp8,p06_reflections,1,p06_reflections__r1__mxfp8,31.907638199998473,3.988454774999809,0.34380390000296757
 nvfp4,p06_reflections,1,p06_reflections__r1__nvfp4,24.88203130000329,3.1102539125004114,0.34354329999769107
+int4_convrot,p06_reflections,1,p06_reflections__r1__int4_convrot,9.523634800003492,1.1904543500004365,0.3413371999995434
+gguf_q8_0,p06_reflections,1,p06_reflections__r1__gguf_q8_0,27.44960120000178,3.4312001500002225,0.748744100004842
+gguf_q4_k_m,p06_reflections,1,p06_reflections__r1__gguf_q4_k_m,31.367689799997606,3.920961224999701,0.34132940000563394
 bf16,p07_foliage_bird,0,p07_foliage_bird__r0__bf16,26.3315333999999,3.2914416749999873,0.36088089999975637
 fp8_scaled,p07_foliage_bird,0,p07_foliage_bird__r0__fp8_scaled,19.66935349999403,2.458669187499254,0.36557429999811575
 int8_convrot,p07_foliage_bird,0,p07_foliage_bird__r0__int8_convrot,12.718108300003223,1.589763537500403,0.3733365000007325
 mxfp8,p07_foliage_bird,0,p07_foliage_bird__r0__mxfp8,32.311203999997815,4.038900499999727,0.3433459000007133
 nvfp4,p07_foliage_bird,0,p07_foliage_bird__r0__nvfp4,24.98653000000195,3.1233162500002436,0.3638533000048483
+int4_convrot,p07_foliage_bird,0,p07_foliage_bird__r0__int4_convrot,9.915689399997063,1.239461174999633,0.3636465000017779
+gguf_q8_0,p07_foliage_bird,0,p07_foliage_bird__r0__gguf_q8_0,28.32509630000277,3.540637037500346,0.7763994999986608
+gguf_q4_k_m,p07_foliage_bird,0,p07_foliage_bird__r0__gguf_q4_k_m,31.77119529999618,3.9713994124995224,0.3591720000040368
 bf16,p07_foliage_bird,1,p07_foliage_bird__r1__bf16,25.66702929999883,3.208378662499854,0.3615896000046632
 fp8_scaled,p07_foliage_bird,1,p07_foliage_bird__r1__fp8_scaled,19.346259199999622,2.4182823999999528,0.34041009999782545
 int8_convrot,p07_foliage_bird,1,p07_foliage_bird__r1__int8_convrot,12.30146319999767,1.5376828999997088,0.35326789999817265
 mxfp8,p07_foliage_bird,1,p07_foliage_bird__r1__mxfp8,31.822317199999816,3.977789649999977,0.3452991000012844
 nvfp4,p07_foliage_bird,1,p07_foliage_bird__r1__nvfp4,24.88409369999863,3.1105117124998287,0.34237080000457354
+int4_convrot,p07_foliage_bird,1,p07_foliage_bird__r1__int4_convrot,9.485797300003469,1.1857246625004336,0.34157060000143247
+gguf_q8_0,p07_foliage_bird,1,p07_foliage_bird__r1__gguf_q8_0,27.325094699997862,3.415636837499733,0.8244144999989658
+gguf_q4_k_m,p07_foliage_bird,1,p07_foliage_bird__r1__gguf_q4_k_m,31.348175700004504,3.918521962500563,0.35404990000097314
 bf16,p08_fog_neon,0,p08_fog_neon__r0__bf16,26.342836100004206,3.2928545125005257,0.3599956999969436
 fp8_scaled,p08_fog_neon,0,p08_fog_neon__r0__fp8_scaled,19.73903650000284,2.467379562500355,0.36486839999997756
 int8_convrot,p08_fog_neon,0,p08_fog_neon__r0__int8_convrot,12.660384799994063,1.5825480999992578,0.3578401999984635
 mxfp8,p08_fog_neon,0,p08_fog_neon__r0__mxfp8,32.0215584999969,4.0026948124996125,0.3458612999966135
 nvfp4,p08_fog_neon,0,p08_fog_neon__r0__nvfp4,24.983396399999037,3.1229245499998797,0.3660890999963158
+int4_convrot,p08_fog_neon,0,p08_fog_neon__r0__int4_convrot,9.527445500003523,1.1909306875004404,0.36525909999909345
+gguf_q8_0,p08_fog_neon,0,p08_fog_neon__r0__gguf_q8_0,28.30532760000642,3.5381659500008027,0.7769578999941587
+gguf_q4_k_m,p08_fog_neon,0,p08_fog_neon__r0__gguf_q4_k_m,31.807442099998298,3.975930262499787,0.35795040000084555
 bf16,p08_fog_neon,1,p08_fog_neon__r1__bf16,25.73160349999671,3.2164504374995886,0.3584536999987904
 fp8_scaled,p08_fog_neon,1,p08_fog_neon__r1__fp8_scaled,19.452148899996246,2.4315186124995307,0.34226489999855403
 int8_convrot,p08_fog_neon,1,p08_fog_neon__r1__int8_convrot,12.371107699997083,1.5463884624996354,0.34410080000088783
 mxfp8,p08_fog_neon,1,p08_fog_neon__r1__mxfp8,31.901877699994657,3.987734712499332,0.3437627000021166
 nvfp4,p08_fog_neon,1,p08_fog_neon__r1__nvfp4,24.880305899998348,3.1100382374997935,0.3463271000000532
+int4_convrot,p08_fog_neon,1,p08_fog_neon__r1__int4_convrot,9.526920600001176,1.190865075000147,0.34329649999563117
+gguf_q8_0,p08_fog_neon,1,p08_fog_neon__r1__gguf_q8_0,27.36883250000392,3.42110406250049,0.8276694000014686
+gguf_q4_k_m,p08_fog_neon,1,p08_fog_neon__r1__gguf_q4_k_m,31.325291399996786,3.9156614249995982,0.34057219999522204
 bf16,p09_material_macro,0,p09_material_macro__r0__bf16,26.355002300006163,3.2943752875007704,0.36106889999791747
 fp8_scaled,p09_material_macro,0,p09_material_macro__r0__fp8_scaled,19.70316059999459,2.4628950749993237,0.36208410000108415
 int8_convrot,p09_material_macro,0,p09_material_macro__r0__int8_convrot,12.728351700003259,1.5910439625004074,0.3715217999997549
 mxfp8,p09_material_macro,0,p09_material_macro__r0__mxfp8,32.009764800000994,4.001220600000124,0.34393160000036005
 nvfp4,p09_material_macro,0,p09_material_macro__r0__nvfp4,25.076785599994764,3.1345981999993455,0.3656614999999874
+int4_convrot,p09_material_macro,0,p09_material_macro__r0__int4_convrot,9.892751899998984,1.236593987499873,0.36523359999409877
+gguf_q8_0,p09_material_macro,0,p09_material_macro__r0__gguf_q8_0,28.314560499995423,3.539320062499428,0.8206292999966536
+gguf_q4_k_m,p09_material_macro,0,p09_material_macro__r0__gguf_q4_k_m,31.804133799996634,3.9755167249995793,0.3585677000010037
 bf16,p09_material_macro,1,p09_material_macro__r1__bf16,25.69524629999796,3.211905787499745,0.35989620000327704
 fp8_scaled,p09_material_macro,1,p09_material_macro__r1__fp8_scaled,19.360541899994132,2.4200677374992665,0.3411396000010427
 int8_convrot,p09_material_macro,1,p09_material_macro__r1__int8_convrot,12.321569399995496,1.540196174999437,0.34455739999975776
 mxfp8,p09_material_macro,1,p09_material_macro__r1__mxfp8,31.863198700004432,3.982899837500554,0.3462238999927649
 nvfp4,p09_material_macro,1,p09_material_macro__r1__nvfp4,24.835008399997605,3.1043760499997006,0.34106790000078036
+int4_convrot,p09_material_macro,1,p09_material_macro__r1__int4_convrot,9.499239699995087,1.1874049624993859,0.34260809999977937
+gguf_q8_0,p09_material_macro,1,p09_material_macro__r1__gguf_q8_0,27.369021099999372,3.4211276374999215,0.7993760000026668
+gguf_q4_k_m,p09_material_macro,1,p09_material_macro__r1__gguf_q4_k_m,31.274367999998503,3.909295999999813,0.3417509999999311
 bf16,p10_vector_gradient,0,p10_vector_gradient__r0__bf16,26.41063660000509,3.3013295750006364,0.3622260999982245
 fp8_scaled,p10_vector_gradient,0,p10_vector_gradient__r0__fp8_scaled,19.791433999998844,2.4739292499998555,0.3632878999997047
 int8_convrot,p10_vector_gradient,0,p10_vector_gradient__r0__int8_convrot,12.803664499995648,1.600458062499456,0.37378200000239303
 mxfp8,p10_vector_gradient,0,p10_vector_gradient__r0__mxfp8,32.07423589999962,4.009279487499953,0.3469769000002998
 nvfp4,p10_vector_gradient,0,p10_vector_gradient__r0__nvfp4,25.130464199995913,3.141308024999489,0.3634611000015866
+int4_convrot,p10_vector_gradient,0,p10_vector_gradient__r0__int4_convrot,9.948575100002927,1.2435718875003658,0.3612795000008191
+gguf_q8_0,p10_vector_gradient,0,p10_vector_gradient__r0__gguf_q8_0,28.394874799996614,3.549359349999577,0.7773340999992797
+gguf_q4_k_m,p10_vector_gradient,0,p10_vector_gradient__r0__gguf_q4_k_m,31.813586500000383,3.976698312500048,0.35644289999618195
 bf16,p10_vector_gradient,1,p10_vector_gradient__r1__bf16,25.740654799999902,3.2175818499999878,0.3583937000003061
 fp8_scaled,p10_vector_gradient,1,p10_vector_gradient__r1__fp8_scaled,19.49589990000095,2.4369874875001187,0.340949700002966
 int8_convrot,p10_vector_gradient,1,p10_vector_gradient__r1__int8_convrot,12.38974589999998,1.5487182374999975,0.35242319999815663
 mxfp8,p10_vector_gradient,1,p10_vector_gradient__r1__mxfp8,31.892903699997987,3.9866129624997484,0.34392099999968195
 nvfp4,p10_vector_gradient,1,p10_vector_gradient__r1__nvfp4,24.889698499995575,3.111212312499447,0.34209610000107205
+int4_convrot,p10_vector_gradient,1,p10_vector_gradient__r1__int4_convrot,9.549842699998408,1.193730337499801,0.3424897000004421
+gguf_q8_0,p10_vector_gradient,1,p10_vector_gradient__r1__gguf_q8_0,27.350974999993923,3.4188718749992404,0.7968674000003375
+gguf_q4_k_m,p10_vector_gradient,1,p10_vector_gradient__r1__gguf_q4_k_m,31.343910000003234,3.9179887500004043,0.35485480000352254
 bf16,p11_retro_anime,0,p11_retro_anime__r0__bf16,26.427372999998624,3.303421624999828,0.36001230000692885
 fp8_scaled,p11_retro_anime,0,p11_retro_anime__r0__fp8_scaled,19.82015749999846,2.4775196874998073,0.3625553999954718
 int8_convrot,p11_retro_anime,0,p11_retro_anime__r0__int8_convrot,12.793321099998138,1.5991651374997673,0.37392710000131046
 mxfp8,p11_retro_anime,0,p11_retro_anime__r0__mxfp8,32.069333699997514,4.008666712499689,0.34537339999951655
 nvfp4,p11_retro_anime,0,p11_retro_anime__r0__nvfp4,25.071917300003406,3.1339896625004258,0.36412510000081966
+int4_convrot,p11_retro_anime,0,p11_retro_anime__r0__int4_convrot,9.938519400006044,1.2423149250007555,0.3625169000006281
+gguf_q8_0,p11_retro_anime,0,p11_retro_anime__r0__gguf_q8_0,28.361656900000526,3.5452071125000657,0.8082552999985637
+gguf_q4_k_m,p11_retro_anime,0,p11_retro_anime__r0__gguf_q4_k_m,31.851024399999005,3.9813780499998757,0.3565096000020276
 bf16,p11_retro_anime,1,p11_retro_anime__r1__bf16,25.738796300000104,3.217349537500013,0.3581162999980734
 fp8_scaled,p11_retro_anime,1,p11_retro_anime__r1__fp8_scaled,19.491088599999784,2.436386074999973,0.3423934000020381
 int8_convrot,p11_retro_anime,1,p11_retro_anime__r1__int8_convrot,12.400373699994816,1.550046712499352,0.3561979000005522
 mxfp8,p11_retro_anime,1,p11_retro_anime__r1__mxfp8,31.915469100000337,3.989433637500042,0.34709660000225995
 nvfp4,p11_retro_anime,1,p11_retro_anime__r1__nvfp4,24.91180919999897,3.1139761499998713,0.3412951999998768
+int4_convrot,p11_retro_anime,1,p11_retro_anime__r1__int4_convrot,9.51476960000582,1.1893462000007275,0.34137059999920893
+gguf_q8_0,p11_retro_anime,1,p11_retro_anime__r1__gguf_q8_0,27.459316400003445,3.4324145500004306,0.8112834999992629
+gguf_q4_k_m,p11_retro_anime,1,p11_retro_anime__r1__gguf_q4_k_m,31.380225699998846,3.9225282124998557,0.3407459999943967
 bf16,p12_watercolor,0,p12_watercolor__r0__bf16,26.340583699995477,3.2925729624994347,0.3605783999955747
 fp8_scaled,p12_watercolor,0,p12_watercolor__r0__fp8_scaled,19.71092110000609,2.463865137500761,0.36248039999918547
 int8_convrot,p12_watercolor,0,p12_watercolor__r0__int8_convrot,12.736942100003944,1.592117762500493,0.37184930000512395
 mxfp8,p12_watercolor,0,p12_watercolor__r0__mxfp8,31.99003079999966,3.9987538499999573,0.3437904000020353
 nvfp4,p12_watercolor,0,p12_watercolor__r0__nvfp4,25.040403200000583,3.130050400000073,0.3656928000054904
+int4_convrot,p12_watercolor,0,p12_watercolor__r0__int4_convrot,9.875884900000528,1.234485612500066,0.3643586999969557
+gguf_q8_0,p12_watercolor,0,p12_watercolor__r0__gguf_q8_0,28.388148400001228,3.5485185500001535,0.8432290999990073
+gguf_q4_k_m,p12_watercolor,0,p12_watercolor__r0__gguf_q4_k_m,31.813851700004307,3.9767314625005383,0.3582428000008804
 bf16,p12_watercolor,1,p12_watercolor__r1__bf16,25.708062799996696,3.213507849999587,0.35866660000465345
 fp8_scaled,p12_watercolor,1,p12_watercolor__r1__fp8_scaled,19.33984490000148,2.417480612500185,0.34357749999617226
 int8_convrot,p12_watercolor,1,p12_watercolor__r1__int8_convrot,12.33782169999904,1.54222771249988,0.3531944999995176
 mxfp8,p12_watercolor,1,p12_watercolor__r1__mxfp8,31.847633999997925,3.9809542499997406,0.34513710000464926
 nvfp4,p12_watercolor,1,p12_watercolor__r1__nvfp4,24.82736699999805,3.103420874999756,0.3418500999978278
+int4_convrot,p12_watercolor,1,p12_watercolor__r1__int4_convrot,9.455161900004896,1.181895237500612,0.3419293999977526
+gguf_q8_0,p12_watercolor,1,p12_watercolor__r1__gguf_q8_0,27.390198200002487,3.423774775000311,0.7497471999959089
+gguf_q4_k_m,p12_watercolor,1,p12_watercolor__r1__gguf_q4_k_m,31.2819559999989,3.9102444999998625,0.3548501000041142
 bf16,p13_food,0,p13_food__r0__bf16,26.28882750000048,3.28610343750006,0.359178200000315
 fp8_scaled,p13_food,0,p13_food__r0__fp8_scaled,19.57829430000129,2.447286787500161,0.36358910000126343
 int8_convrot,p13_food,0,p13_food__r0__int8_convrot,12.59504530000413,1.5743806625005163,0.3585473999992246
 mxfp8,p13_food,0,p13_food__r0__mxfp8,31.915211999999883,3.9894014999999854,0.3469457999963197
 nvfp4,p13_food,0,p13_food__r0__nvfp4,24.89938080000138,3.1124226000001727,0.36475969999446534
+int4_convrot,p13_food,0,p13_food__r0__int4_convrot,9.477282799998648,1.184660349999831,0.36286390000168467
+gguf_q8_0,p13_food,0,p13_food__r0__gguf_q8_0,28.354598399993847,3.544324799999231,0.8509674000015366
+gguf_q4_k_m,p13_food,0,p13_food__r0__gguf_q4_k_m,31.69334220000019,3.9616677750000235,0.3569444000022486
 bf16,p13_food,1,p13_food__r1__bf16,25.687490700001945,3.210936337500243,0.3574178000053507
 fp8_scaled,p13_food,1,p13_food__r1__fp8_scaled,19.317986800000654,2.4147483500000817,0.3451076999990619
 int8_convrot,p13_food,1,p13_food__r1__int8_convrot,12.318170099999406,1.5397712624999258,0.3425465999971493
 mxfp8,p13_food,1,p13_food__r1__mxfp8,31.82090210000024,3.97761276250003,0.3468256000051042
 nvfp4,p13_food,1,p13_food__r1__nvfp4,24.812316700001247,3.101539587500156,0.3419875999970827
+int4_convrot,p13_food,1,p13_food__r1__int4_convrot,9.463287099999434,1.1829108874999292,0.3415798999994877
+gguf_q8_0,p13_food,1,p13_food__r1__gguf_q8_0,27.301244700000098,3.412655587500012,0.827067199999874
+gguf_q4_k_m,p13_food,1,p13_food__r1__gguf_q4_k_m,31.28079080000316,3.910098850000395,0.3431720000007772
 bf16,p14_spatial_counts,0,p14_spatial_counts__r0__bf16,26.303392399997392,3.287924049999674,0.3585942000063369
 fp8_scaled,p14_spatial_counts,0,p14_spatial_counts__r0__fp8_scaled,19.04544380000152,2.38068047500019,0.33408699999563396
 int8_convrot,p14_spatial_counts,0,p14_spatial_counts__r0__int8_convrot,12.620540500000061,1.5775675625000076,0.3720668999958434
 mxfp8,p14_spatial_counts,0,p14_spatial_counts__r0__mxfp8,31.957430399997975,3.994678799999747,0.3476455999989412
 nvfp4,p14_spatial_counts,0,p14_spatial_counts__r0__nvfp4,24.93046460000187,3.1163080750002337,0.3636112000021967
+int4_convrot,p14_spatial_counts,0,p14_spatial_counts__r0__int4_convrot,9.494005199994717,1.1867506499993397,0.3617510000040056
+gguf_q8_0,p14_spatial_counts,0,p14_spatial_counts__r0__gguf_q8_0,28.412769300004584,3.551596162500573,0.7972765000013169
+gguf_q4_k_m,p14_spatial_counts,0,p14_spatial_counts__r0__gguf_q4_k_m,31.819547999999486,3.9774434999999357,0.35713440000108676
 bf16,p14_spatial_counts,1,p14_spatial_counts__r1__bf16,25.75583330000518,3.2194791625006474,0.3613586999999825
 fp8_scaled,p14_spatial_counts,1,p14_spatial_counts__r1__fp8_scaled,19.16530440000497,2.3956630500006213,0.34405169999809004
 int8_convrot,p14_spatial_counts,1,p14_spatial_counts__r1__int8_convrot,12.32397349999519,1.5404966874993988,0.3424712999985786
 mxfp8,p14_spatial_counts,1,p14_spatial_counts__r1__mxfp8,31.860219800000777,3.982527475000097,0.34347580000030575
 nvfp4,p14_spatial_counts,1,p14_spatial_counts__r1__nvfp4,24.90657089999877,3.113321362499846,0.3416189999989001
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metrics/performance_bridge.json ADDED Viewed

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metrics/performance_runs.csv CHANGED Viewed

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metrics/performance_summary.csv CHANGED Viewed

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provenance/manifest.json CHANGED Viewed

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 ]

provenance/runs_scored.jsonl CHANGED Viewed

The diff for this file is too large to render. See raw diff

reproduction/README.md CHANGED Viewed

@@ -6,7 +6,7 @@
 - ComfyUI commit `917faef771a2fd2f14f44af94f17da3d0b2803a3`
 - Python 3.13.12, PyTorch 2.13.0+cu130, CUDA runtime 13.0
 - comfy-kitchen 0.2.18, transformers 5.13.1, NumPy 2.5.1
-- About 77.536 GiB for required model files; at least 110 GiB free is recommended for models, captures, analysis caches, and reports
 Use the pinned ComfyUI commit for a strict reproduction. A newer ComfyUI build may run the workflow but is a different software condition and must be reported as such.
@@ -19,15 +19,25 @@ hf auth login
 python download_models.py --models-dir ../models --all --accept-krea-license
 ```
-The downloader uses `Comfy-Org/Krea-2` revision `8038ce89b91b042141541ad0fa51b985ca262c5f` and verifies all seven SHA-256 hashes. It does not place weights in this dataset repository.
 ## 2. Configure ComfyUI
-Change into `reproduction/benchmark`, copy `config.example.json` to `config.json`, and set:
 - `paths.portable_root`: root containing `ComfyUI/` and, for Windows portable, `python_embeded/python.exe`
 - `paths.models_dir`: directory containing all seven downloaded files
-- `paths.results_dir`: a new output directory
 Copy `extra_model_paths.yaml.example` to `extra_model_paths.yaml`, replace the common parent placeholder, and expose `custom_nodes/krea2_benchmark` through that file. Forward slashes are recommended in YAML on Windows.
@@ -49,7 +59,7 @@ python -m pip install --target .vendor -r requirements-analysis.txt
 ## 4. Execute the campaign
-Run each gate explicitly so failures cannot be hidden:
 ```powershell
 .\run_benchmark.ps1 preflight
@@ -61,7 +71,19 @@ Run each gate explicitly so failures cannot be hidden:
 .\run_benchmark.ps1 verify
 ```
-`all` is available after preflight passes. The campaign performs one unscored warmup and two repeatability checks per format, then generates the 150 scored images. Do not compare formats if the initial-noise hashes, sampler-equivalence check, or completion audit fail.
 ## 5. Rebuild this Hugging Face release
@@ -82,4 +104,4 @@ The builder refuses an existing destination. Move or remove a previous generated
 - Artifact probes measure clipping, gradient behavior, grid boundaries, high-frequency power, flat-region noise, and color change.
 - Weight reconstruction streams each native quantized layout against BF16 and reports element-weighted error, cosine, and SNR.
 - Prompt-alignment and no-reference IQA scores supplement fidelity metrics but do not replace paired BF16 comparisons.
-- GPU speed results are hardware/runtime-specific. MXFP8 and NVFP4 used fallback paths on the tested SM 8.9 GPU.

 - ComfyUI commit `917faef771a2fd2f14f44af94f17da3d0b2803a3`
 - Python 3.13.12, PyTorch 2.13.0+cu130, CUDA runtime 13.0
 - comfy-kitchen 0.2.18, transformers 5.13.1, NumPy 2.5.1
+- About 104 GiB for required model files; at least 150 GiB free is recommended for models, captures, analysis caches, and reports
 Use the pinned ComfyUI commit for a strict reproduction. A newer ComfyUI build may run the workflow but is a different software condition and must be reported as such.
 python download_models.py --models-dir ../models --all --accept-krea-license
 ```
+The downloader uses the exact pinned revisions recorded in `provenance/model_manifest.json`: Comfy-Org for the original checkpoints and shared components, Winnougan for INT4 ConvRot, and vantagewithai for both GGUF files. It verifies all ten SHA-256 hashes and does not place weights in this dataset repository.
 ## 2. Configure ComfyUI
+Install the pinned GGUF loader into ComfyUI:
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/city96/ComfyUI-GGUF.git
+cd ComfyUI-GGUF
+git checkout 6ea2651e7df66d7585f6ffee804b20e92fb38b8a
+python -m pip install gguf==0.19.0
+```
+Change into `reproduction/benchmark`, copy `config_base.example.json` to `config.json`, copy `config_extended.example.json` to `config_extended.json`, and set the common paths in `config.json`:
 - `paths.portable_root`: root containing `ComfyUI/` and, for Windows portable, `python_embeded/python.exe`
 - `paths.models_dir`: directory containing all seven downloaded files
+- `paths.results_dir`: keep `results/krea2_formats_v1`; the extension imports this immutable base into `results/krea2_formats_v2_extended`
 Copy `extra_model_paths.yaml.example` to `extra_model_paths.yaml`, replace the common parent placeholder, and expose `custom_nodes/krea2_benchmark` through that file. Forward slashes are recommended in YAML on Windows.
 ## 4. Execute the campaign
+Run the original five-format base campaign first:
 ```powershell
 .\run_benchmark.ps1 preflight
 .\run_benchmark.ps1 verify
 ```
+Then run the three-format extension and unified eight-format analysis:
+```powershell
+.\run_benchmark.ps1 preflight -Config .\config_extended.json
+.\run_benchmark.ps1 weights -Config .\config_extended.json
+.\run_benchmark.ps1 generate -Config .\config_extended.json
+.\run_benchmark.ps1 analyze -Config .\config_extended.json
+.\run_benchmark.ps1 sheets -Config .\config_extended.json
+.\run_benchmark.ps1 report -Config .\config_extended.json
+.\run_benchmark.ps1 verify -Config .\config_extended.json
+```
+The base campaign produces 150 scored images. The extension hard-links the immutable base captures, runs five BF16 and INT8 bridge repeats, generates 90 new scored images, and analyzes all 240 rows. Do not compare formats if initial-noise hashes, sampler equivalence, bridge evidence, or either completion audit fails.
 ## 5. Rebuild this Hugging Face release
 - Artifact probes measure clipping, gradient behavior, grid boundaries, high-frequency power, flat-region noise, and color change.
 - Weight reconstruction streams each native quantized layout against BF16 and reports element-weighted error, cosine, and SNR.
 - Prompt-alignment and no-reference IQA scores supplement fidelity metrics but do not replace paired BF16 comparisons.
+- GPU speed results are hardware/runtime-specific. MXFP8 and NVFP4 used fallback paths, while GGUF used on-demand dequantized matrix multiplication on the tested SM 8.9 GPU.

reproduction/download_models.py CHANGED Viewed

@@ -21,8 +21,8 @@ def main() -> int:
     parser = argparse.ArgumentParser(description="Download and verify Krea 2 benchmark models")
     parser.add_argument("--models-dir", type=Path, required=True)
     parser.add_argument("--manifest", type=Path, default=Path(__file__).resolve().parents[1] / "provenance" / "model_manifest.json")
-    parser.add_argument("--all", action="store_true", help="Download all five formats plus text encoder and VAE")
-    parser.add_argument("--format", choices=["bf16", "fp8_scaled", "int8_convrot", "mxfp8", "nvfp4"], action="append")
     parser.add_argument("--accept-krea-license", action="store_true")
     args = parser.parse_args()
     if not args.accept_krea_license:
@@ -33,7 +33,7 @@ def main() -> int:
     manifest = json.loads(args.manifest.read_text(encoding="utf-8"))
     args.models_dir.mkdir(parents=True, exist_ok=True)
     common = {"qwen3vl_4b_bf16.safetensors", "qwen_image_vae.safetensors"}
-    wanted = common | {f"krea2_turbo_{fmt}.safetensors" for fmt in selected}
     if args.all:
         wanted = {item["filename"] for item in manifest["files"]}
     for item in manifest["files"]:
@@ -43,7 +43,7 @@ def main() -> int:
         if destination.exists() and digest(destination) == item["sha256"]:
             print(f"verified {destination.name}")
             continue
-        cached = Path(hf_hub_download(repo_id=manifest["repository"], revision=manifest["revision"], filename=item["repository_path"]))
         shutil.copy2(cached, destination)
         actual = digest(destination)
         if actual != item["sha256"]:

     parser = argparse.ArgumentParser(description="Download and verify Krea 2 benchmark models")
     parser.add_argument("--models-dir", type=Path, required=True)
     parser.add_argument("--manifest", type=Path, default=Path(__file__).resolve().parents[1] / "provenance" / "model_manifest.json")
+    parser.add_argument("--all", action="store_true", help="Download all eight formats plus text encoder and VAE")
+    parser.add_argument("--format", choices=["bf16", "fp8_scaled", "int8_convrot", "mxfp8", "nvfp4", "int4_convrot", "gguf_q8_0", "gguf_q4_k_m"], action="append")
     parser.add_argument("--accept-krea-license", action="store_true")
     args = parser.parse_args()
     if not args.accept_krea_license:
     manifest = json.loads(args.manifest.read_text(encoding="utf-8"))
     args.models_dir.mkdir(parents=True, exist_ok=True)
     common = {"qwen3vl_4b_bf16.safetensors", "qwen_image_vae.safetensors"}
+    wanted = common | {item["filename"] for item in manifest["files"] if item.get("format_id") in selected}
     if args.all:
         wanted = {item["filename"] for item in manifest["files"]}
     for item in manifest["files"]:
         if destination.exists() and digest(destination) == item["sha256"]:
             print(f"verified {destination.name}")
             continue
+        cached = Path(hf_hub_download(repo_id=item["repository"], revision=item["revision"], filename=item["repository_path"]))
         shutil.copy2(cached, destination)
         actual = digest(destination)
         if actual != item["sha256"]:

scripts/prepare_release.py CHANGED Viewed

@@ -11,15 +11,18 @@ from pathlib import Path
 from typing import Any, Iterable
-FORMATS = ("bf16", "fp8_scaled", "int8_convrot", "mxfp8", "nvfp4")
 FORMAT_LABELS = {
     "bf16": "BF16",
     "fp8_scaled": "FP8 Scaled",
     "int8_convrot": "INT8 ConvRot",
     "mxfp8": "MXFP8",
     "nvfp4": "NVFP4",
 }
-FIDELITY_ORDER = ("bf16", "int8_convrot", "mxfp8", "fp8_scaled", "nvfp4")
 MODEL_SUBDIRS = {
     "qwen3vl_4b_bf16.safetensors": "text_encoders",
     "qwen_image_vae.safetensors": "vae",
@@ -31,9 +34,7 @@ SCORING_TABLES = {
     "latency": "latency_components.csv",
 }
 IDENTIFIER_COLUMNS = {"run_id", "format_id", "prompt_id", "replicate", "seed", "category"}
-CAPTURE_RE = re.compile(
-    r"^(p\d+_.+)__r(\d+)__(bf16|fp8_scaled|int8_convrot|mxfp8|nvfp4)$"
-)
 ABSOLUTE_WINDOWS_PATH_RE = re.compile(r"[A-Za-z]:\\")
 GPU_UUID_RE = re.compile(r"GPU-[0-9a-fA-F-]{8,}")
 PCI_BUS_RE = re.compile(r"(?:[0-9A-Fa-f]{8}:)?[0-9A-Fa-f]{2}:[0-9A-Fa-f]{2}\.[0-7]")
@@ -109,12 +110,37 @@ def parse_command_json(command_record: dict[str, Any]) -> dict[str, Any]:
         return {}
 def sanitize_configuration(configuration: dict[str, Any]) -> dict[str, Any]:
     cleaned = json.loads(json.dumps(configuration))
     cleaned["paths"] = {
         "portable_root": "<COMFYUI_PORTABLE_ROOT>",
         "models_dir": "<KREA_MODEL_DIRECTORY>",
-        "results_dir": "results/krea2_formats_v1",
     }
     return cleaned
@@ -137,6 +163,14 @@ def sanitize_manifest(source: dict[str, Any]) -> dict[str, Any]:
             "commit_date": git_lines[1] if len(git_lines) > 1 else None,
             "commit_subject": git_lines[2] if len(git_lines) > 2 else None,
         },
         "gpu": {
             "name": gpu_parts[0] if len(gpu_parts) > 0 else runtime.get("device"),
             "driver_version": gpu_parts[2] if len(gpu_parts) > 2 else None,
@@ -154,8 +188,9 @@ def assert_source_complete(results: Path) -> None:
     audit = json_load(results / "validation" / "completion_audit.json")
     if not audit.get("passed") or audit.get("failures"):
         raise RuntimeError("Source completion audit did not pass")
-    if audit.get("scored_runs") != 150:
-        raise RuntimeError(f"Expected 150 scored runs, found {audit.get('scored_runs')}")
     if any(audit.get("format_counts", {}).get(fmt) != 30 for fmt in FORMATS):
         raise RuntimeError("Source format counts are incomplete")
     if not json_load(results / "validation" / "sampler_equivalence.json").get("image_bit_exact"):
@@ -173,30 +208,43 @@ def copy_tree(source: Path, destination: Path) -> None:
 def build_model_manifest(config: dict[str, Any], manifest: dict[str, Any]) -> dict[str, Any]:
     integrity = config["integrity"]
-    revision = integrity["publisher_revision"]
-    repo = integrity["publisher_repository"]
     names = [item["checkpoint"] for item in config["formats"].values()]
     names.extend([config["controls"]["text_encoder"], config["controls"]["vae"]])
     files = []
     for name in names:
         subdir = MODEL_SUBDIRS.get(name, "diffusion_models")
-        remote_path = f"{subdir}/{name}"
         entry = manifest["models"][name]
         files.append(
             {
                 "filename": name,
                 "comfyui_subdirectory": subdir,
                 "repository_path": remote_path,
                 "download_url": f"https://huggingface.co/{repo}/resolve/{revision}/{remote_path}",
                 "bytes": entry["bytes"],
                 "gib": entry["gib"],
                 "sha256": integrity["expected_sha256"][name],
             }
         )
     return {
-        "schema_version": 1,
-        "repository": repo,
-        "revision": revision,
         "license": "Krea 2 Community License",
         "license_url": "https://huggingface.co/krea/Krea-2-Turbo",
         "total_bytes": sum(item["bytes"] for item in files),
@@ -217,12 +265,12 @@ def parse_decision_table(report: Path) -> list[dict[str, str]]:
                         [
                             "format",
                             "file_gib",
-                            "lpips_alex_mean",
-                            "dists_mean",
-                            "dinov2_cosine_mean",
-                            "final_latent_relative_l2_mean",
                             "weight_snr_db",
-                            "sampling_seconds_mean",
                             "peak_vram_mib",
                             "energy_joules_mean",
                         ],
@@ -230,10 +278,10 @@ def parse_decision_table(report: Path) -> list[dict[str, str]]:
                     )
                 )
             )
-        if len(rows) == 5:
             break
-    if len(rows) != 5:
-        raise RuntimeError("Could not parse five-row decision table from technical report")
     return rows
@@ -298,6 +346,12 @@ license: cc-by-4.0
 pretty_name: Krea 2 Turbo ComfyUI Format Fidelity Benchmark
 size_categories:
 - n<1K
 task_categories:
 - text-to-image
 tags:
@@ -312,35 +366,41 @@ tags:
 - int8
 - mxfp8
 - nvfp4
 ---
 # Krea 2 Turbo ComfyUI Format Fidelity Benchmark
-This release is a paired, deterministic comparison of the BF16, FP8 Scaled, INT8 ConvRot, MXFP8, and NVFP4 Krea 2 Turbo checkpoints through native ComfyUI execution. It contains 150 scored 1024×1024 images, all saved float32 decoded tensors and final latents, every denoising trajectory, raw metric tables, scored telemetry, statistical comparisons, and reproducibility code.
-![Five-format contact sheet](comparison_sheets/contact_sheet_replicate0.png)
 ## Main result
 1. **BF16** is the highest-fidelity reference because it is the unquantized published checkpoint.
-2. **INT8 ConvRot** is the best quantized checkpoint by the preregistered LPIPS-Alex endpoint and also leads the other quantized formats on DISTS, DINO similarity, final-latent relative L2, and reconstructed-weight SNR.
-3. **MXFP8** ranks second among quantized formats for fidelity on this campaign.
-4. **FP8 Scaled** ranks third among quantized formats.
-5. **NVFP4** is the smallest checkpoint, but it has the largest measured fidelity loss.
 No weighted composite score is used. See [the full technical report](TECHNICAL_REPORT.md) and [`tables/decision_table.csv`](tables/decision_table.csv).
 ## Important performance limitation
-The test GPU was an NVIDIA GeForce RTX 4060 Ti 16 GB (SM 8.9). In the tested ComfyUI/comfy-kitchen runtime, MXFP8 and NVFP4 native fast matrix multiplication requires SM 10.0, so their measured Ada timings used fallback/dequantized execution and must not be projected to Blackwell hardware.
 ## Dataset organization
 - `data/train/metadata.jsonl` is the Hugging Face ImageFolder index. Each row links an image to its prompt, seed, format, checkpoint provenance, raw scientific artifacts, and flattened metric values.
 - `raw/` contains `decoded_float32.npy`, `final_latent_float32.npy`, `trajectory.npz`, and capture `metadata.json` for every scored run.
 - `metrics/` contains raw per-image, per-parameter, paired-statistics, summary, trajectory, latency, and performance tables.
-- `comparison_sheets/` contains five-format sheets, BF16-relative difference maps, and automatically selected detail crops.
-- `telemetry/` contains only scored GPU and system telemetry.
 - `provenance/` contains sanitized environment, model, run, and release manifests.
 - `reproduction/` contains the ComfyUI workflows, custom capture node, benchmark driver, analyzers, model downloader, and exact instructions.
@@ -349,23 +409,23 @@ Load the image table locally:
 ```python
 from datasets import load_dataset
-dataset = load_dataset("imagefolder", data_dir="data", split="train")
-print(dataset.num_rows)  # 150
 ```
 ## Fixed inference controls
-- 15 prompts × 2 deterministic seeds × 5 checkpoint formats = 150 scored images
 - 1024×1024, batch size 1
 - 8 steps, CFG 1.0, Euler sampler, simple scheduler, denoise 1.0
 - Shared `qwen3vl_4b_bf16.safetensors` text encoder and `qwen_image_vae.safetensors` VAE
 - Zeroed positive conditioning used as negative conditioning
 - No prompt rewriting, LoRAs, previews, upscaling, or post-processing
-- Identical initial-noise SHA-256 for every five-format prompt/seed group
 ## Reproduce or verify
-Follow [`reproduction/README.md`](reproduction/README.md). A faithful full rerun downloads 77.536 GiB of upstream model files and should have at least 110 GiB of free disk space. The published benchmark was tested on Windows 11 with a 16 GB NVIDIA GPU; lower-memory configurations are not certified.
 Validate this downloaded release:
@@ -401,7 +461,7 @@ def build_reproduction_readme() -> str:
 - ComfyUI commit `917faef771a2fd2f14f44af94f17da3d0b2803a3`
 - Python 3.13.12, PyTorch 2.13.0+cu130, CUDA runtime 13.0
 - comfy-kitchen 0.2.18, transformers 5.13.1, NumPy 2.5.1
-- About 77.536 GiB for required model files; at least 110 GiB free is recommended for models, captures, analysis caches, and reports
 Use the pinned ComfyUI commit for a strict reproduction. A newer ComfyUI build may run the workflow but is a different software condition and must be reported as such.
@@ -414,15 +474,25 @@ hf auth login
 python download_models.py --models-dir ../models --all --accept-krea-license
 ```
-The downloader uses `Comfy-Org/Krea-2` revision `8038ce89b91b042141541ad0fa51b985ca262c5f` and verifies all seven SHA-256 hashes. It does not place weights in this dataset repository.
 ## 2. Configure ComfyUI
-Change into `reproduction/benchmark`, copy `config.example.json` to `config.json`, and set:
 - `paths.portable_root`: root containing `ComfyUI/` and, for Windows portable, `python_embeded/python.exe`
 - `paths.models_dir`: directory containing all seven downloaded files
-- `paths.results_dir`: a new output directory
 Copy `extra_model_paths.yaml.example` to `extra_model_paths.yaml`, replace the common parent placeholder, and expose `custom_nodes/krea2_benchmark` through that file. Forward slashes are recommended in YAML on Windows.
@@ -444,7 +514,7 @@ python -m pip install --target .vendor -r requirements-analysis.txt
 ## 4. Execute the campaign
-Run each gate explicitly so failures cannot be hidden:
 ```powershell
 .\run_benchmark.ps1 preflight
@@ -456,7 +526,19 @@ Run each gate explicitly so failures cannot be hidden:
 .\run_benchmark.ps1 verify
 ```
-`all` is available after preflight passes. The campaign performs one unscored warmup and two repeatability checks per format, then generates the 150 scored images. Do not compare formats if the initial-noise hashes, sampler-equivalence check, or completion audit fail.
 ## 5. Rebuild this Hugging Face release
@@ -477,7 +559,7 @@ The builder refuses an existing destination. Move or remove a previous generated
 - Artifact probes measure clipping, gradient behavior, grid boundaries, high-frequency power, flat-region noise, and color change.
 - Weight reconstruction streams each native quantized layout against BF16 and reports element-weighted error, cosine, and SNR.
 - Prompt-alignment and no-reference IQA scores supplement fidelity metrics but do not replace paired BF16 comparisons.
-- GPU speed results are hardware/runtime-specific. MXFP8 and NVFP4 used fallback paths on the tested SM 8.9 GPU.
 """
@@ -505,8 +587,8 @@ def main() -> int:
     parser = argparse.ArgumentParser(description="Download and verify Krea 2 benchmark models")
     parser.add_argument("--models-dir", type=Path, required=True)
     parser.add_argument("--manifest", type=Path, default=Path(__file__).resolve().parents[1] / "provenance" / "model_manifest.json")
-    parser.add_argument("--all", action="store_true", help="Download all five formats plus text encoder and VAE")
-    parser.add_argument("--format", choices=["bf16", "fp8_scaled", "int8_convrot", "mxfp8", "nvfp4"], action="append")
     parser.add_argument("--accept-krea-license", action="store_true")
     args = parser.parse_args()
     if not args.accept_krea_license:
@@ -517,7 +599,7 @@ def main() -> int:
     manifest = json.loads(args.manifest.read_text(encoding="utf-8"))
     args.models_dir.mkdir(parents=True, exist_ok=True)
     common = {"qwen3vl_4b_bf16.safetensors", "qwen_image_vae.safetensors"}
-    wanted = common | {f"krea2_turbo_{fmt}.safetensors" for fmt in selected}
     if args.all:
         wanted = {item["filename"] for item in manifest["files"]}
     for item in manifest["files"]:
@@ -527,7 +609,7 @@ def main() -> int:
         if destination.exists() and digest(destination) == item["sha256"]:
             print(f"verified {destination.name}")
             continue
-        cached = Path(hf_hub_download(repo_id=manifest["repository"], revision=manifest["revision"], filename=item["repository_path"]))
         shutil.copy2(cached, destination)
         actual = digest(destination)
         if actual != item["sha256"]:
@@ -570,7 +652,15 @@ def main() -> int:
         return 0
     api = HfApi()
     api.create_repo(repo_id=args.repo_id, repo_type="dataset", private=args.private, exist_ok=True)
-    api.upload_folder(repo_id=args.repo_id, repo_type="dataset", folder_path=str(root), commit_message="Publish Krea 2 Turbo format benchmark")
     print(f"https://huggingface.co/datasets/{args.repo_id}")
     return 0
@@ -593,7 +683,7 @@ from pathlib import Path
 from urllib.parse import unquote
-FORMATS = ("bf16", "fp8_scaled", "int8_convrot", "mxfp8", "nvfp4")
 def checksum(path: Path) -> str:
@@ -618,8 +708,9 @@ def main() -> int:
     errors: list[str] = []
     metadata_path = root / "data" / "train" / "metadata.jsonl"
     rows = [json.loads(line) for line in metadata_path.read_text(encoding="utf-8").splitlines() if line.strip()]
-    fail(len(rows) != 150, f"metadata rows: expected 150, found {len(rows)}", errors)
-    fail(len({row["run_id"] for row in rows}) != 150, "run_id values are not unique", errors)
     counts = Counter(row["format_id"] for row in rows)
     for fmt in FORMATS:
         fail(counts[fmt] != 30, f"{fmt}: expected 30, found {counts[fmt]}", errors)
@@ -630,25 +721,25 @@ def main() -> int:
             path = base / row[key]
             required_paths.append(path)
             fail(not path.is_file(), f"missing {key}: {path}", errors)
-    fail(len(list((root / "data" / "train" / "images").rglob("*.png"))) != 150, "expected 150 PNG images", errors)
-    fail(len(list((root / "raw").rglob("*.npy"))) != 300, "expected 300 NPY files", errors)
-    fail(len(list((root / "raw").rglob("*.npz"))) != 150, "expected 150 NPZ files", errors)
     fail(len(list((root / "comparison_sheets").rglob("*.*"))) != 93, "expected 93 comparison artifacts", errors)
-    fail(len(list((root / "telemetry").glob("*.csv"))) != 10, "expected 10 scored telemetry files", errors)
     expected_metric_rows = {
-        "image_core.csv": 150,
-        "image_advanced.csv": 150,
-        "latency_components.csv": 150,
-        "performance_runs.csv": 150,
-        "trajectory.csv": 1920,
-        "weight_parameters_all.csv": 1720,
     }
     for filename, expected in expected_metric_rows.items():
         with (root / "metrics" / filename).open("r", encoding="utf-8", newline="") as stream:
             metric_rows = list(csv.DictReader(stream))
         fail(len(metric_rows) != expected, f"{filename}: expected {expected} rows, found {len(metric_rows)}", errors)
         if filename in {"image_core.csv", "image_advanced.csv", "latency_components.csv", "performance_runs.csv"}:
-            fail(len({row["run_id"] for row in metric_rows}) != 150, f"{filename}: run_id values are not unique", errors)
     forbidden_names = {".vendor", "advanced_metric_cache", "logs", "comfy_output", "__pycache__", ".cache", "cache"}
     for path in root.rglob("*"):
         fail(any(part in forbidden_names for part in path.parts), f"forbidden path: {path}", errors)
@@ -706,8 +797,8 @@ def main() -> int:
         try:
             from datasets import load_dataset
-            dataset = load_dataset("imagefolder", data_dir=str(root / "data"), split="train")
-            fail(dataset.num_rows != 150, f"ImageFolder rows: {dataset.num_rows}", errors)
             _ = dataset[0]["image"]
         except Exception as exc:
             errors.append(f"ImageFolder load failed: {exc}")
@@ -728,33 +819,35 @@ if __name__ == "__main__":
 '''
-def build_release(benchmark_root: Path, destination: Path) -> None:
     benchmark_root = benchmark_root.resolve()
-    results = benchmark_root / "results" / "krea2_formats_v1"
     if destination.exists():
         raise FileExistsError(f"Destination already exists: {destination}")
     assert_source_complete(results)
-    config = json_load(benchmark_root / "config.json")
     source_manifest = json_load(results / "manifest.json")
     scored_runs = [row for row in jsonl_load(results / "runs.jsonl") if row.get("scored") and row.get("status") == "complete"]
-    if len(scored_runs) != 150:
-        raise RuntimeError(f"Expected 150 successful scored run records, found {len(scored_runs)}")
     run_by_id = {row["run_id"]: row for row in scored_runs}
     prompt_by_id = {prompt["id"]: prompt for prompt in config["prompts"]}
     metric_maps: dict[str, dict[str, dict[str, str]]] = {}
     for group, filename in SCORING_TABLES.items():
         rows = csv_rows(results / "metrics" / filename)
         metric_maps[group] = {row["run_id"]: row for row in rows}
-        if len(metric_maps[group]) != 150:
-            raise RuntimeError(f"{filename}: expected 150 run rows")
     destination.mkdir(parents=True)
     image_root = destination / "data" / "train" / "images"
     raw_root = destination / "raw"
     metadata_rows: list[dict[str, Any]] = []
     capture_dirs = [path for path in (results / "captures").iterdir() if path.is_dir() and CAPTURE_RE.match(path.name)]
-    if len(capture_dirs) != 150:
-        raise RuntimeError(f"Expected 150 scored capture directories, found {len(capture_dirs)}")
     for capture in sorted(capture_dirs, key=lambda path: path.name):
         match = CAPTURE_RE.match(capture.name)
         assert match
@@ -772,6 +865,7 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
         for filename in ("decoded_float32.npy", "final_latent_float32.npy", "trajectory.npz", "metadata.json"):
             shutil.copy2(capture / filename, release_raw / filename)
         checkpoint = config["formats"][fmt]["checkpoint"]
         row: dict[str, Any] = {
             "file_name": image_relative.as_posix(),
             "run_id": run_id,
@@ -784,8 +878,9 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
             "format_label": FORMAT_LABELS[fmt],
             "checkpoint_filename": checkpoint,
             "checkpoint_sha256": config["integrity"]["expected_sha256"][checkpoint],
-            "publisher_repository": config["integrity"]["publisher_repository"],
-            "publisher_revision": config["integrity"]["publisher_revision"],
             "width": config["controls"]["width"],
             "height": config["controls"]["height"],
             "steps": config["controls"]["steps"],
@@ -812,9 +907,9 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
     copy_tree(results / "metrics", destination / "metrics")
     copy_tree(results / "comparison_sheets", destination / "comparison_sheets")
     (destination / "telemetry").mkdir()
-    for fmt in FORMATS:
-        for name in (f"{fmt}.csv", f"system_{fmt}.csv"):
-            shutil.copy2(results / "telemetry" / name, destination / "telemetry" / name)
     copy_tree(results / "validation", destination / "validation")
     report_text = (results / "TECHNICAL_REPORT.md").read_text(encoding="utf-8")
     report_text = re.sub(
@@ -835,6 +930,8 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
         provenance / "environment-lock.json",
         {
             "comfyui": sanitized_manifest["comfyui"],
             "gpu": sanitized_manifest["gpu"],
             "platform": sanitized_manifest["platform"],
             "runtime": sanitized_manifest["runtime"],
@@ -857,6 +954,8 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
     decision_rows = parse_decision_table(results / "TECHNICAL_REPORT.md")
     write_csv(destination / "tables" / "decision_table.csv", decision_rows)
     ranking_rows = [
         {
             "fidelity_rank": rank,
@@ -865,7 +964,7 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
             "scope": "BF16-relative checkpoint fidelity; no aesthetic composite score",
             "basis": "BF16 reference" if fmt == "bf16" else "LPIPS-Alex primary endpoint with supporting DISTS, DINOv2, latent, and weight evidence",
         }
-        for rank, fmt in enumerate(FIDELITY_ORDER, 1)
     ]
     write_csv(destination / "tables" / "format_ranking.csv", ranking_rows)
     metric_dictionary = build_metric_dictionary(results / "metrics")
@@ -874,7 +973,7 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
         destination / "tables" / "metadata_schema.json",
         {
             "schema_version": 1,
-            "rows": 150,
             "identifiers": ["run_id", "prompt_id", "replicate", "seed", "format_id"],
             "image_field": "file_name",
             "raw_artifact_fields": ["decoded_array_path", "final_latent_path", "trajectory_path", "capture_metadata_path"],
@@ -904,7 +1003,11 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
     shutil.copy2(benchmark_root / "requirements-analysis.txt", benchmark_release / "requirements-analysis.txt")
     (reproduction / "README.md").write_text(build_reproduction_readme(), encoding="utf-8")
     (reproduction / "download_models.py").write_text(build_download_script(), encoding="utf-8")
-    (benchmark_release / "config.example.json").write_text(json.dumps(sanitize_configuration(config), indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
     (benchmark_release / "extra_model_paths.yaml.example").write_text(
         "krea_benchmark:\n  base_path: <COMMON_PARENT_DIRECTORY>\n  diffusion_models: models\n  text_encoders: models\n  vae: models\n  custom_nodes: Huggingface datasets/reproduction/benchmark/custom_nodes\n",
         encoding="utf-8",
@@ -964,7 +1067,7 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
     )
     (destination / "CITATION.cff").write_text(
         "cff-version: 1.2.0\nmessage: If you use this benchmark, please cite it.\ntitle: Krea 2 Turbo ComfyUI Format Fidelity Benchmark\n"
-        "type: dataset\nversion: 1.0.0\ndate-released: 2026-07-13\nauthors:\n  - name: Krea 2 Turbo Formats Benchmark Contributors\n"
         "license: CC-BY-4.0\n",
         encoding="utf-8",
     )
@@ -982,7 +1085,7 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
     release_manifest = {
         "schema_version": 1,
         "benchmark_id": config["benchmark_id"],
-        "dataset_rows": 150,
         "format_counts": dict(Counter(row["format_id"] for row in metadata_rows)),
         "file_counts_by_top_level": dict(sorted(category_counts.items())),
         "payload_bytes_before_checksums": sum(path.stat().st_size for path in files_before_manifest),
@@ -1012,9 +1115,10 @@ def build_release(benchmark_root: Path, destination: Path) -> None:
 def main() -> int:
     parser = argparse.ArgumentParser(description="Build the clean Hugging Face release for the Krea 2 benchmark")
     parser.add_argument("--benchmark-root", type=Path, default=Path(__file__).resolve().parents[1])
     parser.add_argument("--destination", type=Path, required=True)
     args = parser.parse_args()
-    build_release(args.benchmark_root, args.destination.resolve())
     print(args.destination.resolve())
     return 0

 from typing import Any, Iterable
+FORMATS = ("bf16", "fp8_scaled", "int8_convrot", "mxfp8", "nvfp4", "int4_convrot", "gguf_q8_0", "gguf_q4_k_m")
 FORMAT_LABELS = {
     "bf16": "BF16",
     "fp8_scaled": "FP8 Scaled",
     "int8_convrot": "INT8 ConvRot",
     "mxfp8": "MXFP8",
     "nvfp4": "NVFP4",
+    "int4_convrot": "INT4 ConvRot W4A4",
+    "gguf_q8_0": "GGUF Q8_0",
+    "gguf_q4_k_m": "GGUF Q4_K_M",
 }
+FIDELITY_ORDER = ("bf16", "int8_convrot", "gguf_q8_0", "mxfp8", "fp8_scaled", "gguf_q4_k_m", "nvfp4", "int4_convrot")
 MODEL_SUBDIRS = {
     "qwen3vl_4b_bf16.safetensors": "text_encoders",
     "qwen_image_vae.safetensors": "vae",
     "latency": "latency_components.csv",
 }
 IDENTIFIER_COLUMNS = {"run_id", "format_id", "prompt_id", "replicate", "seed", "category"}
+CAPTURE_RE = re.compile(rf"^(p\d+_.+)__r(\d+)__({'|'.join(re.escape(fmt) for fmt in FORMATS)})$")
 ABSOLUTE_WINDOWS_PATH_RE = re.compile(r"[A-Za-z]:\\")
 GPU_UUID_RE = re.compile(r"GPU-[0-9a-fA-F-]{8,}")
 PCI_BUS_RE = re.compile(r"(?:[0-9A-Fa-f]{8}:)?[0-9A-Fa-f]{2}:[0-9A-Fa-f]{2}\.[0-7]")
         return {}
+def deep_merge(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any]:
+    merged = json.loads(json.dumps(base))
+    for key, value in override.items():
+        if key == "extends":
+            continue
+        if isinstance(value, dict) and isinstance(merged.get(key), dict):
+            merged[key] = deep_merge(merged[key], value)
+        else:
+            merged[key] = json.loads(json.dumps(value))
+    return merged
+def load_configuration(path: Path, seen: set[Path] | None = None) -> dict[str, Any]:
+    resolved = path.resolve()
+    seen = set() if seen is None else seen
+    if resolved in seen:
+        raise RuntimeError(f"Configuration inheritance cycle: {resolved}")
+    seen.add(resolved)
+    raw = json_load(resolved)
+    parent_name = raw.get("extends")
+    if not parent_name:
+        return raw
+    return deep_merge(load_configuration(resolved.parent / parent_name, seen), raw)
 def sanitize_configuration(configuration: dict[str, Any]) -> dict[str, Any]:
     cleaned = json.loads(json.dumps(configuration))
     cleaned["paths"] = {
         "portable_root": "<COMFYUI_PORTABLE_ROOT>",
         "models_dir": "<KREA_MODEL_DIRECTORY>",
+        "results_dir": configuration.get("paths", {}).get("results_dir", "results/krea2_formats_v2_extended"),
     }
     return cleaned
             "commit_date": git_lines[1] if len(git_lines) > 1 else None,
             "commit_subject": git_lines[2] if len(git_lines) > 2 else None,
         },
+        "comfyui_gguf": {
+            "commit": source.get("comfyui_gguf", {}).get("stdout") or None,
+            "gguf_python": runtime.get("gguf"),
+        },
+        "lineage": {
+            "base_manifest_sha256": source.get("lineage", {}).get("base_manifest_sha256"),
+            "base_results_dir": "results/krea2_formats_v1" if source.get("lineage") else None,
+        },
         "gpu": {
             "name": gpu_parts[0] if len(gpu_parts) > 0 else runtime.get("device"),
             "driver_version": gpu_parts[2] if len(gpu_parts) > 2 else None,
     audit = json_load(results / "validation" / "completion_audit.json")
     if not audit.get("passed") or audit.get("failures"):
         raise RuntimeError("Source completion audit did not pass")
+    expected_runs = 30 * len(FORMATS)
+    if audit.get("scored_runs") != expected_runs:
+        raise RuntimeError(f"Expected {expected_runs} scored runs, found {audit.get('scored_runs')}")
     if any(audit.get("format_counts", {}).get(fmt) != 30 for fmt in FORMATS):
         raise RuntimeError("Source format counts are incomplete")
     if not json_load(results / "validation" / "sampler_equivalence.json").get("image_bit_exact"):
 def build_model_manifest(config: dict[str, Any], manifest: dict[str, Any]) -> dict[str, Any]:
     integrity = config["integrity"]
     names = [item["checkpoint"] for item in config["formats"].values()]
     names.extend([config["controls"]["text_encoder"], config["controls"]["vae"]])
+    format_by_checkpoint = {item["checkpoint"]: item for item in config["formats"].values()}
+    format_id_by_checkpoint = {item["checkpoint"]: format_id for format_id, item in config["formats"].items()}
     files = []
     for name in names:
         subdir = MODEL_SUBDIRS.get(name, "diffusion_models")
+        format_config = format_by_checkpoint.get(name, {})
+        source = format_config.get("source")
+        if source:
+            repo = source["repository"]
+            revision = source["revision"]
+            remote_path = source["url"].split("/resolve/", 1)[1].split("/", 1)[1]
+            source_url = source["url"]
+        else:
+            repo = integrity["publisher_repository"]
+            revision = integrity["publisher_revision"]
+            remote_path = f"{subdir}/{name}"
+            source_url = f"https://huggingface.co/{repo}/resolve/{revision}/{remote_path}"
         entry = manifest["models"][name]
         files.append(
             {
                 "filename": name,
+                "format_id": format_id_by_checkpoint.get(name),
                 "comfyui_subdirectory": subdir,
+                "repository": repo,
+                "revision": revision,
                 "repository_path": remote_path,
                 "download_url": f"https://huggingface.co/{repo}/resolve/{revision}/{remote_path}",
+                "source_url": source_url,
                 "bytes": entry["bytes"],
                 "gib": entry["gib"],
                 "sha256": integrity["expected_sha256"][name],
             }
         )
     return {
+        "schema_version": 2,
         "license": "Krea 2 Community License",
         "license_url": "https://huggingface.co/krea/Krea-2-Turbo",
         "total_bytes": sum(item["bytes"] for item in files),
                         [
                             "format",
                             "file_gib",
+                            "lpips_alex_median",
+                            "dists_median",
+                            "dinov2_cosine_median",
+                            "final_latent_relative_l2_median",
                             "weight_snr_db",
+                            "sampling_seconds_median",
                             "peak_vram_mib",
                             "energy_joules_mean",
                         ],
                     )
                 )
             )
+        if len(rows) == len(FORMATS):
             break
+    if len(rows) != len(FORMATS):
+        raise RuntimeError(f"Could not parse {len(FORMATS)}-row decision table from technical report")
     return rows
 pretty_name: Krea 2 Turbo ComfyUI Format Fidelity Benchmark
 size_categories:
 - n<1K
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: train
+    path: data/train/**
 task_categories:
 - text-to-image
 tags:
 - int8
 - mxfp8
 - nvfp4
+- int4
+- gguf
+- q8-0
+- q4-k-m
 ---
 # Krea 2 Turbo ComfyUI Format Fidelity Benchmark
+This release is a paired, deterministic comparison of eight Krea 2 Turbo checkpoint formats in ComfyUI: BF16, FP8 Scaled, INT8 ConvRot, MXFP8, NVFP4, INT4 ConvRot W4A4, GGUF Q8_0, and GGUF Q4_K_M. It contains 240 scored 1024×1024 images, saved float32 decoded tensors and final latents, every denoising trajectory, raw metric tables, telemetry, statistical comparisons, and reproduction code.
+![Eight-format contact sheet](comparison_sheets/contact_sheet_replicate0.png)
 ## Main result
 1. **BF16** is the highest-fidelity reference because it is the unquantized published checkpoint.
+2. **INT8 ConvRot** narrowly leads the preregistered LPIPS-Alex endpoint. **GGUF Q8_0** is statistically tied with it on that endpoint (Holm-adjusted permutation p=0.9067) and leads INT8 on DISTS, DINO similarity, and reconstructed-weight SNR.
+3. **GGUF Q8_0** is the highest-fidelity of the three added formats.
+4. **GGUF Q4_K_M** is a storage-quality compromise: 6.972 GiB and much closer to BF16 than INT4 ConvRot, but slow on this Ada GPU under ComfyUI-GGUF dequantized execution.
+5. **INT4 ConvRot W4A4** is the smallest checkpoint and fastest of the three additions on this RTX 4060 Ti, but has the largest measured fidelity loss.
 No weighted composite score is used. See [the full technical report](TECHNICAL_REPORT.md) and [`tables/decision_table.csv`](tables/decision_table.csv).
 ## Important performance limitation
+The test GPU was an NVIDIA GeForce RTX 4060 Ti 16 GB (SM 8.9). MXFP8 and NVFP4 native fast matrix multiplication requires SM 10.0 in the tested runtime. GGUF Q8_0 and Q4_K_M use the pinned ComfyUI-GGUF on-demand dequantized matrix-multiplication path. These Ada timings must not be projected to native Blackwell execution.
+The original five formats and the three additions were measured in separate sessions. Five unscored BF16 and INT8 ConvRot bridge repeats quantify drift. INT8 crossed the preregistered 5% drift threshold, so exact old-versus-new timing comparisons remain caveated; fidelity comparisons remain paired to the same saved BF16 prompt/seed outputs.
 ## Dataset organization
 - `data/train/metadata.jsonl` is the Hugging Face ImageFolder index. Each row links an image to its prompt, seed, format, checkpoint provenance, raw scientific artifacts, and flattened metric values.
 - `raw/` contains `decoded_float32.npy`, `final_latent_float32.npy`, `trajectory.npz`, and capture `metadata.json` for every scored run.
 - `metrics/` contains raw per-image, per-parameter, paired-statistics, summary, trajectory, latency, and performance tables.
+- `comparison_sheets/` contains eight-format sheets, BF16-relative difference maps, and automatically selected detail crops.
+- `telemetry/` contains scored GPU/system telemetry plus the BF16/INT8 bridge telemetry.
 - `provenance/` contains sanitized environment, model, run, and release manifests.
 - `reproduction/` contains the ComfyUI workflows, custom capture node, benchmark driver, analyzers, model downloader, and exact instructions.
 ```python
 from datasets import load_dataset
+dataset = load_dataset("Merserk/Krea-2-Turbo-Checkpoint-Format-Benchmark", split="train")
+print(dataset.num_rows)  # 240
 ```
 ## Fixed inference controls
+- 15 prompts × 2 deterministic seeds × 8 checkpoint formats = 240 scored images
 - 1024×1024, batch size 1
 - 8 steps, CFG 1.0, Euler sampler, simple scheduler, denoise 1.0
 - Shared `qwen3vl_4b_bf16.safetensors` text encoder and `qwen_image_vae.safetensors` VAE
 - Zeroed positive conditioning used as negative conditioning
 - No prompt rewriting, LoRAs, previews, upscaling, or post-processing
+- Identical initial-noise SHA-256 for every eight-format prompt/seed group
 ## Reproduce or verify
+Follow [`reproduction/README.md`](reproduction/README.md). A complete rerun downloads about 104 GiB of upstream model files and should have at least 150 GiB free for models, captures, analysis caches, and reports. The published benchmark was tested on Windows 11 with a 16 GB NVIDIA GPU; lower-memory configurations are not certified.
 Validate this downloaded release:
 - ComfyUI commit `917faef771a2fd2f14f44af94f17da3d0b2803a3`
 - Python 3.13.12, PyTorch 2.13.0+cu130, CUDA runtime 13.0
 - comfy-kitchen 0.2.18, transformers 5.13.1, NumPy 2.5.1
+- About 104 GiB for required model files; at least 150 GiB free is recommended for models, captures, analysis caches, and reports
 Use the pinned ComfyUI commit for a strict reproduction. A newer ComfyUI build may run the workflow but is a different software condition and must be reported as such.
 python download_models.py --models-dir ../models --all --accept-krea-license
 ```
+The downloader uses the exact pinned revisions recorded in `provenance/model_manifest.json`: Comfy-Org for the original checkpoints and shared components, Winnougan for INT4 ConvRot, and vantagewithai for both GGUF files. It verifies all ten SHA-256 hashes and does not place weights in this dataset repository.
 ## 2. Configure ComfyUI
+Install the pinned GGUF loader into ComfyUI:
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/city96/ComfyUI-GGUF.git
+cd ComfyUI-GGUF
+git checkout 6ea2651e7df66d7585f6ffee804b20e92fb38b8a
+python -m pip install gguf==0.19.0
+```
+Change into `reproduction/benchmark`, copy `config_base.example.json` to `config.json`, copy `config_extended.example.json` to `config_extended.json`, and set the common paths in `config.json`:
 - `paths.portable_root`: root containing `ComfyUI/` and, for Windows portable, `python_embeded/python.exe`
 - `paths.models_dir`: directory containing all seven downloaded files
+- `paths.results_dir`: keep `results/krea2_formats_v1`; the extension imports this immutable base into `results/krea2_formats_v2_extended`
 Copy `extra_model_paths.yaml.example` to `extra_model_paths.yaml`, replace the common parent placeholder, and expose `custom_nodes/krea2_benchmark` through that file. Forward slashes are recommended in YAML on Windows.
 ## 4. Execute the campaign
+Run the original five-format base campaign first:
 ```powershell
 .\run_benchmark.ps1 preflight
 .\run_benchmark.ps1 verify
 ```
+Then run the three-format extension and unified eight-format analysis:
+```powershell
+.\run_benchmark.ps1 preflight -Config .\config_extended.json
+.\run_benchmark.ps1 weights -Config .\config_extended.json
+.\run_benchmark.ps1 generate -Config .\config_extended.json
+.\run_benchmark.ps1 analyze -Config .\config_extended.json
+.\run_benchmark.ps1 sheets -Config .\config_extended.json
+.\run_benchmark.ps1 report -Config .\config_extended.json
+.\run_benchmark.ps1 verify -Config .\config_extended.json
+```
+The base campaign produces 150 scored images. The extension hard-links the immutable base captures, runs five BF16 and INT8 bridge repeats, generates 90 new scored images, and analyzes all 240 rows. Do not compare formats if initial-noise hashes, sampler equivalence, bridge evidence, or either completion audit fails.
 ## 5. Rebuild this Hugging Face release
 - Artifact probes measure clipping, gradient behavior, grid boundaries, high-frequency power, flat-region noise, and color change.
 - Weight reconstruction streams each native quantized layout against BF16 and reports element-weighted error, cosine, and SNR.
 - Prompt-alignment and no-reference IQA scores supplement fidelity metrics but do not replace paired BF16 comparisons.
+- GPU speed results are hardware/runtime-specific. MXFP8 and NVFP4 used fallback paths, while GGUF used on-demand dequantized matrix multiplication on the tested SM 8.9 GPU.
 """
     parser = argparse.ArgumentParser(description="Download and verify Krea 2 benchmark models")
     parser.add_argument("--models-dir", type=Path, required=True)
     parser.add_argument("--manifest", type=Path, default=Path(__file__).resolve().parents[1] / "provenance" / "model_manifest.json")
+    parser.add_argument("--all", action="store_true", help="Download all eight formats plus text encoder and VAE")
+    parser.add_argument("--format", choices=["bf16", "fp8_scaled", "int8_convrot", "mxfp8", "nvfp4", "int4_convrot", "gguf_q8_0", "gguf_q4_k_m"], action="append")
     parser.add_argument("--accept-krea-license", action="store_true")
     args = parser.parse_args()
     if not args.accept_krea_license:
     manifest = json.loads(args.manifest.read_text(encoding="utf-8"))
     args.models_dir.mkdir(parents=True, exist_ok=True)
     common = {"qwen3vl_4b_bf16.safetensors", "qwen_image_vae.safetensors"}
+    wanted = common | {item["filename"] for item in manifest["files"] if item.get("format_id") in selected}
     if args.all:
         wanted = {item["filename"] for item in manifest["files"]}
     for item in manifest["files"]:
         if destination.exists() and digest(destination) == item["sha256"]:
             print(f"verified {destination.name}")
             continue
+        cached = Path(hf_hub_download(repo_id=item["repository"], revision=item["revision"], filename=item["repository_path"]))
         shutil.copy2(cached, destination)
         actual = digest(destination)
         if actual != item["sha256"]:
         return 0
     api = HfApi()
     api.create_repo(repo_id=args.repo_id, repo_type="dataset", private=args.private, exist_ok=True)
+    api.upload_large_folder(
+        repo_id=args.repo_id,
+        repo_type="dataset",
+        folder_path=str(root),
+        private=args.private,
+        ignore_patterns=[".cache/**", "**/__pycache__/**", "*.pyc"],
+        num_workers=4,
+        print_report_every=30,
+    )
     print(f"https://huggingface.co/datasets/{args.repo_id}")
     return 0
 from urllib.parse import unquote
+FORMATS = ("bf16", "fp8_scaled", "int8_convrot", "mxfp8", "nvfp4", "int4_convrot", "gguf_q8_0", "gguf_q4_k_m")
 def checksum(path: Path) -> str:
     errors: list[str] = []
     metadata_path = root / "data" / "train" / "metadata.jsonl"
     rows = [json.loads(line) for line in metadata_path.read_text(encoding="utf-8").splitlines() if line.strip()]
+    expected_rows = 30 * len(FORMATS)
+    fail(len(rows) != expected_rows, f"metadata rows: expected {expected_rows}, found {len(rows)}", errors)
+    fail(len({row["run_id"] for row in rows}) != expected_rows, "run_id values are not unique", errors)
     counts = Counter(row["format_id"] for row in rows)
     for fmt in FORMATS:
         fail(counts[fmt] != 30, f"{fmt}: expected 30, found {counts[fmt]}", errors)
             path = base / row[key]
             required_paths.append(path)
             fail(not path.is_file(), f"missing {key}: {path}", errors)
+    fail(len(list((root / "data" / "train" / "images").rglob("*.png"))) != expected_rows, f"expected {expected_rows} PNG images", errors)
+    fail(len(list((root / "raw").rglob("*.npy"))) != expected_rows * 2, f"expected {expected_rows * 2} NPY files", errors)
+    fail(len(list((root / "raw").rglob("*.npz"))) != expected_rows, f"expected {expected_rows} NPZ files", errors)
     fail(len(list((root / "comparison_sheets").rglob("*.*"))) != 93, "expected 93 comparison artifacts", errors)
+    fail(len(list((root / "telemetry").glob("*.csv"))) != 20, "expected 20 scored/bridge telemetry files", errors)
     expected_metric_rows = {
+        "image_core.csv": 240,
+        "image_advanced.csv": 240,
+        "latency_components.csv": 240,
+        "performance_runs.csv": 240,
+        "trajectory.csv": 3360,
+        "weight_parameters_all.csv": 3010,
     }
     for filename, expected in expected_metric_rows.items():
         with (root / "metrics" / filename).open("r", encoding="utf-8", newline="") as stream:
             metric_rows = list(csv.DictReader(stream))
         fail(len(metric_rows) != expected, f"{filename}: expected {expected} rows, found {len(metric_rows)}", errors)
         if filename in {"image_core.csv", "image_advanced.csv", "latency_components.csv", "performance_runs.csv"}:
+            fail(len({row["run_id"] for row in metric_rows}) != expected_rows, f"{filename}: run_id values are not unique", errors)
     forbidden_names = {".vendor", "advanced_metric_cache", "logs", "comfy_output", "__pycache__", ".cache", "cache"}
     for path in root.rglob("*"):
         fail(any(part in forbidden_names for part in path.parts), f"forbidden path: {path}", errors)
         try:
             from datasets import load_dataset
+            dataset = load_dataset(str(root), split="train")
+            fail(dataset.num_rows != expected_rows, f"ImageFolder rows: {dataset.num_rows}", errors)
             _ = dataset[0]["image"]
         except Exception as exc:
             errors.append(f"ImageFolder load failed: {exc}")
 '''
+def build_release(benchmark_root: Path, destination: Path, config_path: Path) -> None:
     benchmark_root = benchmark_root.resolve()
+    config_path = config_path if config_path.is_absolute() else benchmark_root / config_path
+    config = load_configuration(config_path)
+    results = benchmark_root / config["paths"]["results_dir"]
+    expected_rows = len(config["prompts"]) * int(config["campaign"]["replicates"]) * len(FORMATS)
     if destination.exists():
         raise FileExistsError(f"Destination already exists: {destination}")
     assert_source_complete(results)
     source_manifest = json_load(results / "manifest.json")
     scored_runs = [row for row in jsonl_load(results / "runs.jsonl") if row.get("scored") and row.get("status") == "complete"]
+    if len(scored_runs) != expected_rows:
+        raise RuntimeError(f"Expected {expected_rows} successful scored run records, found {len(scored_runs)}")
     run_by_id = {row["run_id"]: row for row in scored_runs}
     prompt_by_id = {prompt["id"]: prompt for prompt in config["prompts"]}
     metric_maps: dict[str, dict[str, dict[str, str]]] = {}
     for group, filename in SCORING_TABLES.items():
         rows = csv_rows(results / "metrics" / filename)
         metric_maps[group] = {row["run_id"]: row for row in rows}
+        if len(metric_maps[group]) != expected_rows:
+            raise RuntimeError(f"{filename}: expected {expected_rows} run rows")
     destination.mkdir(parents=True)
     image_root = destination / "data" / "train" / "images"
     raw_root = destination / "raw"
     metadata_rows: list[dict[str, Any]] = []
     capture_dirs = [path for path in (results / "captures").iterdir() if path.is_dir() and CAPTURE_RE.match(path.name)]
+    if len(capture_dirs) != expected_rows:
+        raise RuntimeError(f"Expected {expected_rows} scored capture directories, found {len(capture_dirs)}")
     for capture in sorted(capture_dirs, key=lambda path: path.name):
         match = CAPTURE_RE.match(capture.name)
         assert match
         for filename in ("decoded_float32.npy", "final_latent_float32.npy", "trajectory.npz", "metadata.json"):
             shutil.copy2(capture / filename, release_raw / filename)
         checkpoint = config["formats"][fmt]["checkpoint"]
+        source = config["formats"][fmt].get("source", {})
         row: dict[str, Any] = {
             "file_name": image_relative.as_posix(),
             "run_id": run_id,
             "format_label": FORMAT_LABELS[fmt],
             "checkpoint_filename": checkpoint,
             "checkpoint_sha256": config["integrity"]["expected_sha256"][checkpoint],
+            "publisher_repository": source.get("repository", config["integrity"]["publisher_repository"]),
+            "publisher_revision": source.get("revision", config["integrity"]["publisher_revision"]),
+            "checkpoint_source_url": source.get("url"),
             "width": config["controls"]["width"],
             "height": config["controls"]["height"],
             "steps": config["controls"]["steps"],
     copy_tree(results / "metrics", destination / "metrics")
     copy_tree(results / "comparison_sheets", destination / "comparison_sheets")
     (destination / "telemetry").mkdir()
+    for source in sorted((results / "telemetry").glob("*.csv")):
+        if source.stem in set(FORMATS) | {f"system_{fmt}" for fmt in FORMATS} or source.stem.startswith(("bridge_", "system_bridge_")):
+            shutil.copy2(source, destination / "telemetry" / source.name)
     copy_tree(results / "validation", destination / "validation")
     report_text = (results / "TECHNICAL_REPORT.md").read_text(encoding="utf-8")
     report_text = re.sub(
         provenance / "environment-lock.json",
         {
             "comfyui": sanitized_manifest["comfyui"],
+            "comfyui_gguf": sanitized_manifest["comfyui_gguf"],
+            "lineage": sanitized_manifest["lineage"],
             "gpu": sanitized_manifest["gpu"],
             "platform": sanitized_manifest["platform"],
             "runtime": sanitized_manifest["runtime"],
     decision_rows = parse_decision_table(results / "TECHNICAL_REPORT.md")
     write_csv(destination / "tables" / "decision_table.csv", decision_rows)
+    decision_by_label = {row["format"]: row for row in decision_rows}
+    fidelity_order = sorted(FORMATS, key=lambda fmt: float(decision_by_label[FORMAT_LABELS[fmt]]["lpips_alex_median"]))
     ranking_rows = [
         {
             "fidelity_rank": rank,
             "scope": "BF16-relative checkpoint fidelity; no aesthetic composite score",
             "basis": "BF16 reference" if fmt == "bf16" else "LPIPS-Alex primary endpoint with supporting DISTS, DINOv2, latent, and weight evidence",
         }
+        for rank, fmt in enumerate(fidelity_order, 1)
     ]
     write_csv(destination / "tables" / "format_ranking.csv", ranking_rows)
     metric_dictionary = build_metric_dictionary(results / "metrics")
         destination / "tables" / "metadata_schema.json",
         {
             "schema_version": 1,
+            "rows": expected_rows,
             "identifiers": ["run_id", "prompt_id", "replicate", "seed", "format_id"],
             "image_field": "file_name",
             "raw_artifact_fields": ["decoded_array_path", "final_latent_path", "trajectory_path", "capture_metadata_path"],
     shutil.copy2(benchmark_root / "requirements-analysis.txt", benchmark_release / "requirements-analysis.txt")
     (reproduction / "README.md").write_text(build_reproduction_readme(), encoding="utf-8")
     (reproduction / "download_models.py").write_text(build_download_script(), encoding="utf-8")
+    base_config = load_configuration(benchmark_root / "config.json")
+    (benchmark_release / "config_base.example.json").write_text(json.dumps(sanitize_configuration(base_config), indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
+    extended_template = json_load(config_path)
+    extended_template["extends"] = "config.json"
+    (benchmark_release / "config_extended.example.json").write_text(json.dumps(extended_template, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
     (benchmark_release / "extra_model_paths.yaml.example").write_text(
         "krea_benchmark:\n  base_path: <COMMON_PARENT_DIRECTORY>\n  diffusion_models: models\n  text_encoders: models\n  vae: models\n  custom_nodes: Huggingface datasets/reproduction/benchmark/custom_nodes\n",
         encoding="utf-8",
     )
     (destination / "CITATION.cff").write_text(
         "cff-version: 1.2.0\nmessage: If you use this benchmark, please cite it.\ntitle: Krea 2 Turbo ComfyUI Format Fidelity Benchmark\n"
+        "type: dataset\nversion: 2.0.0\ndate-released: 2026-07-13\nauthors:\n  - name: Krea 2 Turbo Formats Benchmark Contributors\n"
         "license: CC-BY-4.0\n",
         encoding="utf-8",
     )
     release_manifest = {
         "schema_version": 1,
         "benchmark_id": config["benchmark_id"],
+        "dataset_rows": expected_rows,
         "format_counts": dict(Counter(row["format_id"] for row in metadata_rows)),
         "file_counts_by_top_level": dict(sorted(category_counts.items())),
         "payload_bytes_before_checksums": sum(path.stat().st_size for path in files_before_manifest),
 def main() -> int:
     parser = argparse.ArgumentParser(description="Build the clean Hugging Face release for the Krea 2 benchmark")
     parser.add_argument("--benchmark-root", type=Path, default=Path(__file__).resolve().parents[1])
+    parser.add_argument("--config", type=Path, default=Path("config_extended.json"))
     parser.add_argument("--destination", type=Path, required=True)
     args = parser.parse_args()
+    build_release(args.benchmark_root, args.destination.resolve(), args.config)
     print(args.destination.resolve())
     return 0